Improved material detection with stereo beam profile analysis

Abstract

A detector (110) for determining at least one material property of at least one object (112) is proposed. The detector (110) comprises: - at least one projector (116) configured to illuminate the object (112) with at least one illumination pattern (118) including a plurality of illumination features (120); - at least one first camera (122) having at least one first sensor element, the first sensor element having a matrix of first optical sensors, each first optical sensor having a photosensitive area, each first optical sensor designed to generate at least one sensor signal in response to illumination of the respective photosensitive area by a reflected light beam propagating from the object (112) to the first camera (122), the first camera (122) configured to capture at least one first reflected image comprising a plurality of first reflected features generated by the object (112) in response to illumination by the illumination features (120), the first camera (122) being arranged such that the first reflected image is captured in a first line of sight direction relative to the object (112); at least one second camera (124) having at least one second sensor element, the second sensor element having a matrix of second optical sensors, each second optical sensor having a photosensitive area, each second optical sensor designed to generate at least one sensor signal in response to illumination of the respective photosensitive area by a reflected light beam propagating from the object (112) to the second camera (124), the second camera (124) being configured to capture at least one second reflected image comprising a plurality of second reflected features generated by the object (112) in response to illumination by the illumination features (120), the second camera (124) being arranged such that the second reflected image is captured in a second line of sight direction relative to the object (112), the second camera being different from the first line of sight direction and the second line of sight direction; - at least one evaluation device (126) configured to evaluate the first and second reflected images, the evaluation comprising matching the first and second reflected features and determining combined material properties of the matched pair of first and second reflected features by analysis of their beam profiles; Equipped with.

Description

[Technical field]

[0001] The present invention relates to a detector for determining at least one material property, a method for determining material properties, a mobile device and various methods of use. The device, method and method of use according to the invention can be used in particular in various fields, for example in everyday life, security technology, gaming, traffic technology, production technology, photography, such as digital photography or video photography for art, documentation or technical purposes, safety technology, information technology, agriculture, crop protection, maintenance, cosmetics, medical technology or in science. However, other applications are also possible. [Background technology]

[0002] Depth measurements using beam profile analysis (BPA), also called depth ratio technique (DPR) from photons, are commonly known. For beam profile analysis, see WO2018 / 091649A1, WO2018 / 091638A1, WO2018 / 091640A1, and C. Lennartz, F. Schick, S. Metz, "Whitepaper-Beam Profile Analysis for 3D imaging and material detection", April 28, 2021, Ludwigshafen, Germany, the entire contents of which are incorporated by reference.

[0003] Material classification and / or identification using beam profile analysis is known, for example from WO2020 / 187719. For example, material classification and / or identification may be used in machine vision, medical and security applications, for image classification purposes or in gesture recognition algorithms.

[0004] The BPA 3D measurement system takes into account the beam profile and extracts relevant material information. The BPA 3D measurement system has one camera and one projector.

[0005] WO2020 / 187719 describes a detector for identifying at least one material characteristic m. The detector comprises at least one sensor element including a matrix of optical sensors. The optical sensors each have a photosensitive area. The sensor elements are configured to record at least one reflected image of the light beam originating from at least one object. The detector comprises at least one evaluation device configured to determine the material characteristic by evaluation of at least one beam profile of the reflected image. The evaluation device is configured to determine at least one distance characteristic by applying at least one distance-dependent image filter to the reflected image. The evaluation device is configured to determine at least one material characteristic by applying at least one material-dependent image filter to the reflected image. The evaluation device is configured to determine an ordinate z and a material characteristic m by evaluating the distance characteristic and the material characteristic.

[0006] However, despite the above achievements, reliable material classification and / or identification remains an ongoing challenge. [Prior art documents] [Patent documents]

[0007] [Patent Document 1] WO2018 / 091649A1 [Patent Document 2] WO2018 / 091638A1 [Patent Document 3] WO2018 / 091640A1 [Patent Document 4] WO2020 / 187719 [Non-patent literature]

[0008] [Non-Patent Document 1] C. Lennartz, F. Schick, S. Metz, "Whitepaper-Beam Profile Analysis for 3D imaging and material detection", April 28, 2021, Ludwigshafen, Germany Summary of the Invention [Problem to be solved by the invention]

[0009] It is therefore an object of the present invention to provide a device and a method which overcomes the above mentioned technical problems of known devices and methods, in particular, it is an object of the present invention to provide a device and a method which are able to reliably identify at least one material property of an object, preferably with a low technical effort and with low demands in terms of technical resources and costs. [Means for solving the problem]

[0010] This problem is solved by the invention with the features of the independent patent claims. Advantageous developments of the invention, which can be realized individually or in combination, are set out in the dependent claims and / or in the following description and detailed embodiments.

[0011] In a first aspect of the present invention, a detector for determining at least one material property of at least one object is disclosed.

[0012] As used herein, the term "detector" is a broad term and is intended to have the usual and customary meaning to those skilled in the art, and is not limited to a specific or special meaning. The term may specifically refer to, but is not limited to, any sensor device configured to determine and / or detect and / or sense at least one object. The detector may be a fixed device or a mobile device. Furthermore, the detector may be a stand-alone device or may form part of another device, such as a computer, a vehicle, or other device. Furthermore, the detector may be a portable device. Other embodiments of the detector are possible. The detector may be either attached to or incorporated in a mobile device, such as a mobile phone or a smartphone. The detector may be incorporated in the mobile device, for example, within the housing of the mobile device. Additionally or alternatively, the detector or at least one component of the detector may be attached to the mobile device, such as by using a connector, such as a USB, or a phone connector, such as a headphone jack.

[0013] As used herein, the term "object" is a broad term and is intended to have the usual and customary meaning to those skilled in the art, and is not limited to any special or particular meaning. The term may specifically refer to any object, particularly a surface or area, configured to at least partially perform one or more of reflection and / or scattering and / or emission in response to at least one light beam impinging on the object, but is not limited thereto. The light beam may originate from a projector that illuminates the object, and the light beam is reflected and / or scattered by the object. For example, the object may be at least one object selected from the group consisting of a scene, a human being such as a person, an animal such as wood, carpet, foam, cow, plant, tissue piece, metal, toy, metal object, beverage, food such as fruit, meat, fish, dish, makeup, applied makeup, fabric, fur, hair, maintenance product, cream, oil, powder, carpet, juice, suspension, paint, plant, body, body part, organic material, inorganic material, reflective material, screen, display, wall, paper such as a photograph.

[0014] As used herein, the term "material property" is a broad term and is intended to have the usual and customary meaning for those skilled in the art, and is not limited to any special or particular meaning. The term may specifically refer to, but is not limited to, at least one property of a material that is adapted to characterize and / or identify and / or classify the material. For example, the material property may be a property selected from the group consisting of roughness, penetration depth of light into the material, a property that characterizes the material as a biological or non-biological material, reflectance, specular reflectance, diffuse reflectance, surface properties, a measure of translucency, scattering, particularly backscattering behavior, and the like. The at least one material property may be a property selected from the group consisting of scattering coefficient, translucency, transparency, deviation from Lambertian surface reflection, speckle, and the like.

[0015] As used herein, the term "determining at least one material characteristic" is a broad term and is intended to have the ordinary and customary meaning to one of ordinary skill in the art, and is not limited to a specific or special meaning. The term may specifically refer to, but is not limited to, one or more of identifying a material characteristic, characterizing a material characteristic, and assigning a material characteristic to an object. The detector may include at least one database including a list and / or table, such as a look-up list or look-up table, of predefined and / or predetermined material characteristics. The list and / or table of material characteristics may be determined and / or generated by performing at least one test measurement using the detector, for example, by performing material testing using samples with known material characteristics. The list and / or table of material characteristics may be determined and / or generated at the manufacturer site and / or by a user of the detector. The material properties may be further assigned to a material classifier such as one or more of a material name, a material group such as biological or non-biological, translucent or non-translucent, metal or non-metal, skin or non-skin, fur or non-fur, carpet or non-carpet, reflective or non-reflective, specular or non-specular, foam or non-foam, hair or non-hair, roughness group, etc. The detector may include at least one database including lists and / or tables including material properties and associated material names and / or material groups.

[0016] The detector is - at least one projector configured to illuminate an object with at least one illumination pattern including a plurality of illumination features; - at least one first camera having at least one first sensor element, the first sensor element having a matrix of first optical sensors, each first optical sensor having a photosensitive area, each first optical sensor designed to generate at least one sensor signal in response to illumination of the respective photosensitive area by a reflected light beam propagating from the object to the first camera, the first camera configured to capture at least one first reflected image comprising a plurality of first reflected features generated by the object in response to illumination by the illumination features, the first camera being arranged such that the first reflected image is captured in a first line of sight direction relative to the object; - at least one second camera having at least one second sensor element, the second sensor element having a matrix of second optical sensors, each second optical sensor having a photosensitive area, each second optical sensor designed to generate at least one sensor signal in response to illumination of the respective photosensitive area by a reflected light beam propagating from the object to the second camera, the second camera being configured to capture at least one second reflected image comprising a plurality of second reflection features generated by the object in response to illumination by the illumination features, the second camera being arranged such that the second reflected image is captured in a second line of sight direction relative to the object, the second camera being different from the first line of sight direction and the second line of sight direction; - at least one evaluation device configured to evaluate the first and second reflected images, the evaluation comprising matching the first and second reflected features and determining combined material properties of the matched pair of first and second reflected features by analysis of their beam profiles; Equipped with.

[0017] As used herein, the term "projector", also referred to as a floodlight, is a broad term and is intended to have the usual and customary meaning to those skilled in the art, and is not limited to any specific or special meaning. The term may specifically, but not exclusively, refer to an optical device configured to project at least one illumination pattern onto an object, and in particular onto a surface of the object. The projector is configured to illuminate at least one object with at least one illumination pattern that includes a plurality of illumination features.

[0018] As used herein, the term "pattern" is a broad term and is intended to have the usual and customary meaning to those skilled in the art, and is not limited to any special or special meaning. The term may specifically refer to, but is not limited to, any known or predefined arrangement including a plurality of any shaped features, such as symbols. A pattern may include a plurality of features. A pattern may include a periodic or non-periodic arrangement of features. As used herein, the term "at least one illumination pattern" is a broad term and is intended to have the usual and customary meaning to those skilled in the art, and is not limited to any special or special meaning. The term may specifically refer to, but is not limited to, any at least one pattern including illumination features adapted to illuminate at least one portion of an object.

[0019] As used herein, the term "illumination feature" is a broad term and is intended to have the ordinary and customary meaning to one of ordinary skill in the art, and is not limited to any particular or special meaning. The term may specifically refer to, but is not limited to, at least one at least partially extended feature of a pattern. The illumination pattern includes a plurality of illumination features. For example, the illumination pattern includes at least 4000 illumination features or even more illumination features.

[0020] The irradiation pattern may be a regular, in particular a periodic pattern, however other non-regular patterns may also be possible.

[0021] The illumination pattern may include at least one pattern selected from the group consisting of at least one quasi-random pattern, at least one Sobol pattern, at least one quasi-periodic pattern, at least one point pattern, in particular a pseudo-random point pattern, at least one line pattern, at least one stripe pattern, at least one checkerboard pattern, at least one triangular pattern, at least one rectangular pattern, at least one hexagonal pattern, or a pattern including further convex tiling. The illumination pattern may exhibit at least one illumination feature selected from the group consisting of at least one point, at least one line, at least two lines such as parallel lines or crossing lines, at least one point and one line, at least one sequence of periodic features, at least one pattern of any shaped features. For example, the illumination pattern includes at least one pattern including at least one pre-known feature. For example, the illumination pattern includes at least one line pattern including at least one line. For example, the illumination pattern includes at least one line pattern including at least two lines such as parallel lines or crossing lines. For example, the projector may be configured to generate and / or project a point cloud or non-point-like features, such that the illumination pattern may include multiple point or non-point-like features.

[0022] Further, as used herein, the term "illuminating an object with at least one irradiation pattern" may refer to providing at least one irradiation pattern for illuminating at least one object.

[0023] As used herein, the term "light ray" is a broad term and is intended to have the usual and customary meaning for those skilled in the art, and is not limited to any special or special meaning. This term can specifically, but not limited to, refer to a line perpendicular to the wavefront of light that points in the direction of energy flow. As used herein, the term "beam" is a broad term and is intended to have the usual and customary meaning for those skilled in the art, and is not limited to any special or special meaning. This term can specifically, but not limited to, refer to a collection of light rays. Hereinafter, the terms "light ray" and "beam" are used synonymously. As used herein, the term "light beam" is a broad term and is intended to have the usual and customary meaning for those skilled in the art, and is not limited to any special or special meaning. This term can specifically, but not limited to, refer to an amount of light, specifically an amount of light traveling in essentially the same direction, including the possibility that the light beam has a wide angle or divergence angle. The light beam can have a spatial extent. In particular, the light beam can have a non-Gaussian beam profile. The beam profile may be selected from the group consisting of a trapezoidal beam profile, a triangular beam profile, and a conical beam profile. The trapezoidal beam profile may have a plateau region and at least one edge region. The light beam may specifically be a Gaussian light beam or a linear combination of Gaussian light beams, as outlined in more detail below. However, other embodiments are possible.

[0024] The projector may include at least one emitter and / or at least one array of emitters, each of which may be configured to emit at least one light beam.

[0025] The light beams generated by the emitter may generally propagate parallel to the optical axis or at an angle to the optical axis, for example at an angle with the optical axis. The detector may be configured such that one or more light beams propagate from the detector towards the object along the optical axis of the detector. To this end, the detector may include at least one reflecting element, preferably at least one prism, for deflecting the light beam onto the optical axis. By way of example, the light beams and the optical axis may include an angle of less than 10 degrees, preferably less than 5 degrees, or even less than 2 degrees. However, other embodiments are feasible. Furthermore, the light beams may be on the optical axis or off the optical axis. By way of example, the one or more light beams may be parallel to the optical axis with a distance of less than 10 millimeters to the optical axis, preferably less than 5 millimeters to the optical axis, or even coincident with the optical axis.

[0026] As used herein, the term "emitter" is a broad term and is intended to have its ordinary and customary meaning to one of ordinary skill in the art, and is not intended to be limited to any specific or special meaning. Specifically, the term can refer to, but is not limited to, at least one device configured to provide at least one light beam for illumination of an object. Each of the emitters may be and / or may include at least one element selected from the group consisting of at least one laser source, such as at least one semiconductor laser, at least one double heterostructure laser, at least one external cavity laser, at least one separate confinement heterostructure laser, at least one quantum cascade laser, at least one distributed Bragg reflector laser, at least one polariton laser, at least one hybrid silicon laser, at least one extended cavity diode laser, at least one quantum dot laser, at least one volume Bragg grating laser, at least one indium arsenide laser, at least one gallium arsenide laser, at least one transistor laser, at least one diode pumped laser, at least one distributed feedback laser, at least one quantum well laser, at least one interband cascade laser, at least one semiconductor ring laser, at least one vertical cavity surface emitting laser (VCSEL), at least one non-laser light source, such as at least one LED or at least one light bulb.

[0027] The array of emitters may be a two-dimensional or one-dimensional array. The array may include a plurality of emitters arranged in a matrix. As used herein, the term "matrix" is a broad term and is intended to have the usual and customary meaning for those skilled in the art, and is not limited to any special or particular meaning. The term may specifically refer to, but is not limited to, an arrangement of a plurality of elements in a predefined geometric order. The matrix may specifically be or include a rectangular matrix having one or more rows and one or more columns. The rows and columns may specifically be arranged in a rectangular shape. However, other arrangements, such as non-rectangular arrangements, are feasible. By way of example, a circular arrangement is also feasible, in which the elements are arranged in concentric circles or ellipses around a central point.

[0028] For example, the emitter may be an array of VCSELs. As used herein, the term "vertical cavity surface emitting laser" is a broad term and is intended to have the usual and customary meaning to those skilled in the art, and is not limited to any special or special meaning. The term may specifically refer to, but is not limited to, a semiconductor laser diode configured to emit a laser beam perpendicular to a top surface. Examples of VCSELs can be found, for example, at en.wikipedia.org / wiki / Vertical-cavity_surface-emitting_laser. VCSELs are generally known to those skilled in the art, such as from WO2017 / 222618A. Each of the VCSELs is configured to generate at least one light beam. The VCSELs may be arranged on a common substrate or on different substrates. The array may include up to 2500 VCSELs. For example, the array may include 38x25 VCSELs, such as a high power array at 3.5W. For example, the array may include 10x27 VCSELs at 2.5W. For example, an array may include 96 VCSELs at 0.9 W. The size of an array of, for example, 2500 elements, may be up to 2 millimeters by 2 millimeters.

[0029] The light beam emitted by each emitter may have a wavelength of 300 to 1100 nanometers, preferably 500 to 1100 nanometers. For example, the light beam may have a wavelength of 940 nanometers. Light in the infrared spectral range may be used, for example in the range of 780 nanometers to 3.0 μm. In particular, light in the part of the near infrared range, in particular in the range of 700 nanometers to 1100 nanometers, for which silicon photodiodes are applicable, may be used. The emitters may be configured to generate at least one illumination pattern in the infrared range, in particular in the near infrared range. Using light in the near infrared range may enable light that is not detectable by the human eye or is only weakly detectable, but can be detected by a silicon sensor, in particular a standard silicon sensor. For example, the emitters may be an array of VCSELs. The VCSELs may be configured to emit light beams in the wavelength range of 800 to 1000 nanometers. For example, the VCSEL may be configured to emit a light beam at 808 nanometers, 850 nanometers, 940 nanometers, or 980 nanometers. Preferably, the VCSEL emits light at 940 nanometers because terrestrial solar radiation has a local minimum irradiance at this wavelength, as described, for example, in CIE 085-1989 "Solar spectral Irradiance."

[0030] The projector may include at least one transfer device, which is configured to generate illumination characteristics from a light beam impinging on the transfer device. The term "transfer device", also referred to as "transfer system", may generally refer to one or more optical elements adapted to modify a light beam by modifying one or more of the beam parameters of the light beam, the width of the light beam, or the direction of the light beam. The transfer device may include at least one imaging optical device. The transfer device may specifically include one or more of at least one lens, e.g., at least one lens selected from the group consisting of at least one adjustable focus lens, at least one aspheric lens, at least one spherical lens, at least one Fresnel lens, at least one diffractive optical element, at least one concave mirror, at least one beam deflection element, preferably at least one mirror, at least one beam splitting element, preferably at least one of a beam splitting cube or a beam splitting mirror, at least one multi-lens system, at least one holographic optical element, at least one meta-optical element. In particular, the transfer device includes at least one refractive optical lens stack. Thus, the transfer device may include a multi-lens system having refractive properties.

[0031] The projector may include at least one diffractive optical element (DOE) configured to generate an illumination pattern. The DOE may be configured to generate multiple light beams from a single incident light beam.

[0032] The object may include at least one surface onto which the illumination pattern is projected. The surface may be adapted to at least partially reflect the illumination pattern towards the detector. For example, without wishing to be bound by theory, human skin may have a reflection profile, also called a backscattering profile, that includes a portion generated by the back reflection of the surface, called the surface reflection, and a portion generated by a very diffuse reflection from the light penetrating the skin, called the diffuse portion of the back reflection. For the reflection profile of human skin, see "Lasertechnik in der Medizin: Grundlagen, Systeme, Anwendungen", "Wirkung von Laserstrahlung auf Gewebe", 1991, pp. 171-266, Juergen Eichler, Theo Seiler, Springer Verlag, ISBN 0939-0979. The surface reflection of the skin may increase as the wavelength increases towards the near infrared. Furthermore, the penetration depth may increase as the wavelength increases from visible light towards the near infrared. The diffuse portion of the back reflection may increase with the penetration depth of the light. These material properties can be used to distinguish skin from other materials, specifically by analyzing the inverse scattering profile.

[0033] The first camera has at least one first sensor element with a matrix of first optical sensors. Each of the first optical sensors has a light-sensitive area. Each of the first optical sensors is designed to generate at least one sensor signal in response to illumination of the respective light-sensitive area by a light beam propagating from the object to the first camera. The second camera has at least one second sensor element with a matrix of second optical sensors. Each of the second optical sensors has a light-sensitive area. Each of the second optical sensors is designed to generate at least one sensor signal in response to illumination of the respective light-sensitive area by a light beam propagating from the object to the second camera.

[0034] The first camera may include at least one CCD sensor or at least one CMOS sensor. The second camera may include at least one CCD sensor or at least one CMOS sensor.

[0035] The first and second cameras can be designed the same or differently. For example, the first and second cameras can have the same resolution or different resolutions. In the following, the terms "camera", "sensor element" and "optical sensor" without the "first" and "second" numerals refer to valid and / or possible elements, definitions and embodiments of the first and second cameras.

[0036] As used herein, the term "camera" is a broad term and is intended to have the usual and customary meaning to those skilled in the art, and is not limited to a specific or special meaning. The term may specifically refer to, but is not limited to, a device having at least one imaging element configured to record or capture spatially resolved one-dimensional, two-dimensional, or even three-dimensional optical data or information. The camera may be a digital camera. By way of example, the camera may include at least one camera chip, such as at least one CCD chip and / or at least one CMOS chip, configured to record an image. The camera may be or include at least one near-infrared camera.

[0037] As used herein, the term "image" may specifically relate to data recorded by using a camera, such as a number of electronic readings from an imaging device, such as the pixels of a camera chip. The image itself may include pixels, which are correlated to pixels of a matrix of sensor elements. Thus, when referring to a "pixel," one is either referring to a unit of image information generated by a single pixel of a sensor element, or directly to a single pixel of a sensor element. The image may be at least one two-dimensional image. As used herein, the term "two-dimensional image" may generally refer to an image having information about abscissas, such as height and width dimensions. The image may be an RGB (red-green-blue) image. However, other embodiments are possible.

[0038] In addition to at least one camera or imaging chip, the camera may include one or more further elements such as optical elements, e.g., one or more lenses. By way of example, the camera may be a fixed focus camera having at least one lens that is fixedly adjusted relative to the camera. However, the camera may alternatively include one or more variable lenses that can be adjusted automatically or manually.

[0039] The camera may be a camera of a mobile device such as a laptop computer, a tablet, or specifically a mobile phone such as a smartphone. Thus, specifically, the camera may be part of a mobile device that includes one or more data processing devices, such as one or more data processors, in addition to the camera. However, other cameras are feasible. As used herein, the term "mobile device" is a broad term and is intended to have the usual and customary meaning to those skilled in the art, and is not limited to a specific or special meaning. The term may specifically refer to, but is not limited to, a mobile electronic device, more specifically a mobile communication device such as a mobile phone or a smartphone. Additionally or alternatively, the mobile device may also refer to a tablet computer or another type of portable computer.

[0040] As used herein, the term "sensor element" may generally refer to a device or a combination of devices configured to sense at least one parameter. In this case, the parameter may specifically be an optical parameter, and the sensor element may specifically be an optical sensor element. The sensor element may be formed as a unitary, single device or as a combination of several devices. The matrix may specifically be or include a rectangular matrix having one or more rows and one or more columns. The rows and columns may specifically be arranged in a rectangular shape. However, other arrangements are possible, such as non-rectangular arrangements. By way of example, a circular arrangement is also possible, in which the elements are arranged in concentric circles or ellipses around a central point. For example, the matrix may be a single row of pixels. Other arrangements are possible.

[0041] As used herein, an "optical sensor" may generally refer to a photosensitive device for detecting a light beam, such as for detecting illumination and / or a light spot generated by at least one light beam. As further used herein, a "photosensitive area" generally refers to an area of ​​an optical sensor that can be externally illuminated by at least one light beam and generates at least one sensor signal in response to said illumination. The photosensitive area may specifically be arranged on the surface of the respective optical sensor. However, other embodiments are feasible. The optical sensors of the matrix may specifically be equal in one or more of size, sensitivity, and other optical, electrical, and mechanical properties. The photosensitive areas of all optical sensors of the matrix may specifically be arranged in a common plane, which preferably faces the object, such that the light beam propagating from the object to the detector may generate a light spot on the common plane.

[0042] As used herein, the term "optical sensor having at least one photosensitive area" refers to a configuration including a plurality of single optical sensors each having one photosensitive area, and a configuration including one combined optical sensor having multiple photosensitive areas. Thus, the term "optical sensor" also refers to a photosensitive device configured to generate one output signal, whereas in this specification, a photosensitive device configured to generate two or more output signals, e.g., at least one CCD and / or CMOS device, is referred to as two or more optical sensors. Each optical sensor may be embodied such that there is exactly one photosensitive area in each optical sensor, such as by providing exactly one photosensitive area that can be illuminated in response to illumination to generate exactly one uniform sensor signal for the entire optical sensor. Thus, each optical sensor may be a single-area optical sensor. However, the use of a single-area optical sensor makes the setup of the detector particularly simple and efficient. Thus, by way of example, commercially available photo sensors, such as commercially available silicon photodiodes, each having exactly one photosensitive area, may be used in the setup. However, other embodiments are feasible. Thus, by way of example, optical devices comprising two, three, four or more than four photosensitive areas, which are considered as two, three, four or more than four optical sensors, can be used in the context of the present invention. As outlined above, the sensor element comprises a matrix of optical sensors. Thus, by way of example, the optical sensor can be part of or constitute a pixelated optical device. By way of example, the optical sensor can be part of or constitute at least one CCD and / or CMOS device having a matrix of pixels, each pixel forming a photosensitive area.

[0043] The optical sensor may in particular be or include a photodetector, preferably an inorganic photodetector, more preferably an inorganic semiconductor photodetector, most preferably a silicon photodetector. In particular, the optical sensor may be sensitive in the infrared spectral range. All of the optical sensors of the matrix, or at least one group of the optical sensors of the matrix, may in particular be identical. Groups of identical optical sensors of the matrix may in particular be provided for different spectral ranges, or all optical sensors may be identical with respect to their spectral sensitivity. Furthermore, the optical sensors may be identical with respect to size and / or their electronic or optoelectronic properties.

[0044] In particular, the optical sensor may be or include an inorganic photodiode sensitive in the infrared spectral range, preferably in the range of 780 nanometers to 3.0 micrometers. In particular, the optical sensor may be sensitive in the part of the near infrared range, particularly in the range of 700 nanometers to 1000 nanometers, where silicon photodiodes are applicable. Infrared optical sensors that may be used in the optical sensor may be commercially available infrared optical sensors, such as the infrared optical sensors commercially available under the brand name Hertzstueck™ of trinamiX GmbH, D-67056 Ludwigshafen am Rhein, Germany. Thus, by way of example, the optical sensor may include at least one optical sensor of the internal photovoltaic type, more preferably at least one semiconductor photodiode selected from the group consisting of Ge photodiodes, InGaAs photodiodes, extended InGaAs photodiodes, InAs photodiodes, InSb photodiodes, HgCdTe photodiodes. Additionally or alternatively, the optical sensor may comprise at least one optical sensor of the external photovoltaic type, more preferably at least one semiconductor photodiode selected from the group consisting of Ge:Au photodiode, Ge:Hg photodiode, Ge:Cu photodiode, Ge:Zn photodiode, Si:Ga photodiode, Si:As photodiode, Additionally or alternatively, the optical sensor may comprise at least one bolometer, preferably selected from the group consisting of VO bolometers and amorphous Si bolometers.

[0045] The matrix may consist of independent optical sensors, thus consisting of inorganic photodiodes, but alternatively one or more of commercially available matrices may be used, for example CCD detectors, such as CCD detector chips, and / or CMOS detectors, such as CMOS detector chips.

[0046] The optical sensors of the detector may form a sensor array or may be part of a sensor array such as the matrix mentioned above. Thus, by way of example, the detector may include an array of optical sensors such as a rectangular array having m rows and n columns, m, n being independently positive integers. Preferably, there are two or more columns and two or more rows, i.e. n>1, m>1. Thus, by way of example, n may be 2 to 16 or more, and m may be 2 to 16 or more. It is preferred that the ratio between the number of rows and the number of columns is close to 1. By way of example, n and m may be selected such that 0.3≦m / n≦3, such as by selecting m / n=1:1, 4:3, 16:9 or similar. By way of example, the array may be a square array with an equal number of rows and columns, such as by selecting m=2, n=2 or m=3, n=3, etc.

[0047] The matrix may in particular be a rectangular matrix having at least one row, preferably several rows and several columns. By way of example, the rows and columns may be oriented essentially vertically, with reference to the above definition for the term "essentially vertical". Thus, by way of example, a tolerance of less than 20 degrees, in particular less than 10 degrees, or even less than 5 degrees may be acceptable. In order to provide a wide range of views, the matrix may in particular have at least 10 rows, preferably at least 50 rows, more preferably at least 100 rows. Likewise, the matrix may have at least 10 columns, preferably at least 50 columns, more preferably at least 100 columns. The matrix may include at least 50 optical sensors, preferably at least 100 optical sensors, more preferably at least 500 optical sensors. The matrix may include a number of pixels in the multi-mega pixel range. However, other embodiments are possible. Thus, in configurations where axial rotational symmetry is expected, a circular or concentric arrangement of the optical sensors of the matrix, which may also be called pixels, may be preferred.

[0048] The reflected light beams may propagate from the object towards the first and second cameras. The reflected light beams may originate from the object. The projector may illuminate the object with at least one illumination pattern, where the light is attenuated, reflected and / or scattered by the object and thereby directed at least in part as "reflected light beams" towards the first and second cameras.

[0049] The optical sensor may be sensitive in one or more of the ultraviolet, visible, or infrared spectral ranges. Specifically, the optical sensor may be sensitive in the visible spectral range of 500 nanometers to 780 nanometers, most preferably 650 nanometers to 750 nanometers, or 690 nanometers to 700 nanometers. Specifically, the optical sensor may be sensitive in the near infrared range. Specifically, the optical sensor may be sensitive in the portion of the near infrared range where silicon photodiodes are applicable, particularly in the range of 700 nanometers to 1000 nanometers. The optical sensor may be specifically sensitive in the infrared spectral range, particularly in the range of 780 nanometers to 3.0 micrometers. For example, each optical sensor may independently be or include at least one element selected from the group consisting of a photodiode, a photocell, a photoconductor, a phototransistor, or any combination thereof. For example, the optical sensor may be or may include at least one element selected from the group consisting of a CCD sensor element, a CMOS sensor element, a photodiode, a photocell, a photoconductor, a phototransistor, or any combination thereof. Any other type of photosensitive element may be used. The photosensitive element may generally be made entirely or partially from inorganic materials and / or entirely or partially from organic materials. Most commonly, one or more photodiodes, such as commercially available photodiodes, e.g., inorganic semiconductor photodiodes, may be used.

[0050] Further, as used herein, a "sensor signal" generally refers to a signal generated by an optical sensor in response to illumination by a light beam. Specifically, the sensor signal may be or include at least one electrical signal, such as at least one analog electrical signal and / or at least one digital electrical signal. More specifically, the sensor signal may be or include at least one voltage signal and / or at least one current signal. More specifically, the sensor signal may include at least one photocurrent. Furthermore, raw sensor signals may be used, or a detector, optical sensor, or other element may be configured to process or pre-process the sensor signal, such as pre-processing by filtering, thereby generating a secondary sensor signal that can also be used as a sensor signal.

[0051] The light beam generated by the object, also called reflected light beam, may in particular completely illuminate the sensor element, such that the sensor element is completely located within the light beam, with the width of the light beam being larger than the matrix. Conversely, the reflected light beam may in particular preferably produce a light spot over the entire matrix that is smaller than the matrix, such that the light spot is completely located within the matrix. This situation may be easily adjusted by a person skilled in the art of optical systems by selecting one or more suitable lenses or elements that have a focusing or defocusing effect on the light beam, such as by using a suitable transfer device.

[0052] The first camera may include a first forwarding device configured to direct the light beam onto a first optical sensor and form a first reflected image on the first sensor element, and the second camera may include a second forwarding device configured to direct the light beam onto a second optical sensor and form a second reflected image on the second sensor element.

[0053] The first camera is configured to capture at least one first reflected image including a plurality of first reflective features generated by the object in response to illumination by the illumination feature. The second camera is configured to capture at least one second reflected image including a plurality of second reflective features generated by the object in response to illumination by the illumination feature. As used herein, the term "reflected image" may generally refer to an image determined by a respective camera including a plurality of reflective features. As used herein, the term "reflective feature" may generally refer to a feature in an image plane generated by the object in response to illumination by the at least one illumination feature. The reflected image may include at least one reflection pattern including the reflective features. As used herein, the term "capturing at least one reflected image" may refer to one or more of capturing, recording, and generating a reflected image.

[0054] The first camera is arranged such that a first reflected image is captured in a first line of sight direction relative to the object. The second camera is arranged such that a second reflected image is captured in a second line of sight direction relative to the object. The first line of sight direction and the second line of sight direction are different.

[0055] The detector may include an optical axis. For example, the first transfer device may constitute a coordinate system, with the ordinate z being the coordinate along the optical axis of the first transfer device. The coordinate system may be a polar coordinate system, in which the optical axis of the first transfer device forms the z axis, and the distance and polar angle from the z axis may be used as additional coordinates. For example, the first transfer device may constitute a coordinate system, in which the optical axis of the detector forms the z axis, and in addition, an x-axis and a y-axis may be provided that are perpendicular to the z axis and perpendicular to each other. By way of example, the detector may be at a certain point in this coordinate system, such as the origin of this coordinate system. A direction parallel or anti-parallel to the z axis may be considered as a vertical direction, and a coordinate along the z axis may be considered as a vertical coordinate. Any direction perpendicular to the z axis may be considered as a horizontal direction, and a polar coordinate and / or a polar angle may be considered as a horizontal coordinate. Alternatively, other types of coordinate systems may be used. Thus, by way of example, a polar coordinate system may be used, in which the optical axis forms the z axis, and the distance and polar angle from the z axis may be used as additional coordinates. Any direction parallel or anti-parallel to the z-axis can be considered as a longitudinal direction, and any coordinate along the z-axis can be considered as an ordinate. Any direction perpendicular to the z-axis can be considered as a transverse direction, and any polar coordinate and / or polar angle can be considered as an abscissa.

[0056] As used herein, the term "view direction relative to an object", also referred to as "view direction", is a broad term and is intended to have the usual and customary meaning to those skilled in the art, and is not limited to any special or particular meaning. The term may specifically refer to, but is not limited to, the three-dimensional position and / or orientation of each camera in space. The view direction may be defined by the 3D coordinates of the camera, such as x, y, and z. The view direction may be defined by a rotation of the camera, for example, around one or more of the x-axis, y-axis, and z-axis. The rotation may be given by a spatial angle.

[0057] The first and second cameras may be positioned at different spatial locations. The relative distance between the first and second cameras may be fixed. For example, the first and second cameras may be arranged as a stereo camera in the housing of the detector. The detector may be configured such that the first camera may be positioned on one side of the projector and the second camera may be positioned on the opposite side of the projector. For example, the baseline between the first camera and the projector may be shorter than the baseline between the second camera and the projector. As used herein, the term "baseline" is a broad term and indicates the ordinary and customary meaning to those skilled in the art and is not limited to a special or special meaning. The term may specifically refer to, but is not limited to, the relative distance between each camera and the projector. The baseline between the second camera and the projector may be, for example, four times, five times, or more, larger than the baseline between the first camera and the projector. For example, the baseline between the first camera and the projector may be 4 cm or less. For example, the baseline between the first camera and the projector may be 2 cm to 4 cm.

[0058] The photosensitive areas of the first and second cameras may be particularly directed towards the object. As used herein, the term "directed towards the object" generally refers to a situation in which the respective surfaces of the photosensitive areas are fully or partially visible from the object. In particular, at least one interconnection line between at least one point of the object and at least one point of the respective photosensitive area may form an angle with a surface element of the photosensitive area different from 0 degrees, for example an angle in the range of 20 degrees to 90 degrees, preferably 80 degrees to 90 degrees, for example an angle of 90 degrees. Thus, when the object is located on or near the optical axis, the light beam propagating from the object towards the detector may be essentially parallel to the optical axis.

[0059] For example, the first and second cameras have different orientations towards the object. For example, the first sensor element may be oriented essentially perpendicular to the optical axis of the detector. The line of sight of the first camera may be essentially parallel to the optical axis of the detector. As used herein, the term "essentially perpendicular" refers to a state of perpendicular orientation, e.g., with a tolerance of ±20 degrees or less, preferably with a tolerance of ±10 degrees or less, more preferably with a tolerance of ±5 degrees or less. Similarly, the term "essentially parallel" may refer to a state of parallel orientation, e.g., with a tolerance of ±20 degrees or less, preferably with a tolerance of ±10 degrees or less, more preferably with a tolerance of ±5 degrees or less. For example, the second sensor element may be oriented to deviate from an essentially perpendicular alignment to the optical axis of the detector. The line of sight of the second camera may not be essentially parallel to the optical axis of the detector and may form an angle with the optical axis.

[0060] The evaluation device is configured to evaluate the first reflected image and the second reflected image.

[0061] The term "evaluation device" may generally refer to any device adapted to perform specified operations, preferably by using at least one data processing device, more preferably by using at least one processor and / or at least one application specific integrated circuit. Thus, by way of example, the at least one evaluation device may include at least one data processing device, on which a software code comprising several computer commands is stored. The evaluation device may provide one or more hardware elements for performing one or more of the specified operations and / or may provide one or more processors having software running thereon for performing one or more of the specified operations. The operations include evaluating an image. Thus, by way of example, one or more instructions may be implemented in software and / or hardware. Thus, by way of example, the evaluation device may include one or more computers, application specific integrated circuits (ASICs), digital signal processors (DSPs) or one or more programmable devices such as field programmable gate arrays (FPGAs) configured to perform the above evaluations. However, in addition or instead, the evaluation device may be embodied fully or partly by hardware.

[0062] The evaluation device may be or include one or more integrated circuits, such as one or more application specific integrated circuits (ASICs), and / or one or more computers, preferably one or more microcomputers, and / or one or more data processing devices, such as microcontrollers, field programmable gate arrays, or digital signal processors. Additional components may be included, such as one or more pre-processing devices and / or data acquisition devices, e.g. one or more devices for receiving and / or pre-processing the sensor signals, such as one or more analog-to-digital converters and / or one or more filters. Furthermore, the evaluation device may include one or more measuring devices, such as one or more measuring devices for measuring current and / or voltage. Furthermore, the evaluation device may include one or more data storage devices. Furthermore, the evaluation device may include one or more interfaces, e.g. one or more wireless interfaces and / or one or more wired interfaces.

[0063] The evaluation device may be connected to or include at least one further data processing device that may be used for one or more of displaying, visualizing, analyzing, disseminating, communicating, or further processing information, such as information obtained by the optical sensor and / or the evaluation device. The data processing device may be connected to or incorporate at least one of a display, a projector, a monitor, an LCD, a TFT, a loudspeaker, a multi-channel sound system, an LED pattern, or a further visualization device, as examples. The data processing device may further be connected to or incorporate at least one of a communication device or communication interface, connector, or port, capable of sending encrypted or unencrypted information using one or more of an email, text message, phone, Bluetooth, Wi-Fi, infrared, or internet interface, port, or connection. The data processing device may further be connected to or incorporate at least one of the following: a processor, a graphics processor, a CPU, an Open Multimedia Applications Platform (OMAP™), an integrated circuit, a system on a chip such as the Apple A-series or Samsung S3C2-series products, a microcontroller or microprocessor, one or more memory blocks such as ROM, RAM, EEPROM or flash memory, a timing source such as an oscillator or a phase-locked loop, a counter timer, a real-time timer or a power-on reset generator, a voltage regulator, a power management circuit or a DMA controller. The individual units may further be connected by a bus such as an AMBA bus or may be incorporated into an Internet of Things or Industry 4.0 type network.

[0064] The evaluation device and / or the data processing device may be connected by or have a serial or parallel interface or port, a further external interface or port such as one or more of USB, Centronics Port, FireWire, HDMI, Ethernet, Bluetooth, RFID, Wi-Fi, USART, or SPI, or an analog interface or port such as one or more of a standardized interface or port to a further device such as an ADC or DAC, or a 2D camera device using an RGB interface such as CameraLink. The evaluation device and / or the data processing device may be further connected by one or more of a processor-to-processor interface or port, an FPGA-FPGA-interface, or a serial or parallel interface port. The evaluation device and the data processing device may be further connected to one or more of an optical disk drive, a CD-RW drive, a DVD+RW drive, a flash drive, a memory card, a disk drive, a hard disk drive, a solid-state disk, or a solid-state hard disk.

[0065] The evaluation device and / or the data processing device may be connected by or may have one or more further external connectors, such as one or more of a phone connector, an RCA connector, a VGA connector, a hermaphrodite connector, a USB connector, an HDMI connector, an 8P8C connector, a BCN connector, an IEC60320 C14 connector, a fiber optic connector, a D-subminiature connector, an RF connector, a coaxial connector, a SCART connector, an XLR connector, and / or may incorporate at least one suitable socket for one or more of these connectors.

[0066] The evaluation device may be configured to identify and / or select a first reflection feature in the first reflection image and a second reflection feature in the second reflection image. The evaluation device may be configured to perform at least one image analysis and / or image processing to identify and / or select the first reflection feature and the second reflection feature. The image analysis and / or image processing may use at least one feature detection algorithm. The image analysis and / or image processing may include: filtering, selecting at least one region of interest, forming a difference image between an image generated by the sensor signal and at least one offset, inverting the sensor signal by inverting the image generated by the sensor signal, forming a difference image between images generated by the sensor signal at different times, background correction, decomposition into color channels, decomposition into hue, saturation, luminance channel, frequency decomposition, singular value decomposition, applying a blob detector, applying a corner detector, applying a determinant of a Hessian filter, applying a principal curvature based region detector, applying a maximum stable extremum region detector, applying a generalized Hough transform, applying a ridge detector, applying an affine invariant feature detector, applying an affine adaptive interest point operator, applying a Harris affine region detector, applying a Hessian affine region detector, applying a scale invariant feature transform, scale space The method may include one or more of: applying an extreme value detector, applying a local feature detector, applying a fast robust feature algorithm, applying a histogram of gradient position and orientation algorithm, applying a histogram of oriented gradient descriptors, applying a Deriche edge detector, applying a differential edge detector, applying a spatio-temporal interest point detector, applying a Moravec corner detector, applying a Canny edge detector, applying a Laplace operator of Gaussian filters, applying a difference of Gaussian filters, applying a Sobel operator, applying a Laplacian operator, applying a Scharr operator, applying a Prewitt operator, applying a Roberts operator, applying a Kirsch operator, applying a high pass filter, applying a low pass filter, applying a Fourier transform, applying a Radon transform, applying a Hough transform, applying a wavelet transform, thresholding, and generating a binary image. In particular, evaluating the reflectance image includes selecting a region of interest in the reflectance image.The region of interest may be determined manually by a user or automatically, such as by recognizing an object in an image produced by the sensor elements. For example, in the case of a spot-like reflective feature, the region of interest may be selected as the area around the spot profile.

[0067] The evaluation device may be configured to perform at least one image correction, which may include at least one background subtraction, and the evaluation device may be adapted to remove background light effects from the respective beam profile, for example by imaging without further illumination.

[0068] The evaluation includes matching the first reflection feature with the second reflection feature. As used herein, the term "matching" is a broad term and is intended to have a normal and customary meaning for a person skilled in the art, and is not limited to a special or special meaning. The term may specifically refer to, but is not limited to, determining and / or evaluating and / or identifying corresponding first and second reflection features. Matching may include solving a so-called correspondence problem. As used herein, the term "corresponding reflection features" may refer to each of the first reflection features having a corresponding second reflection feature generated by the same illumination feature. The evaluation device may be configured to unambiguously match the first reflection feature with the second reflection feature. As used herein, the term "unambiguously match" may refer to only one first reflection feature being assigned to one second reflection feature, and / or no other first reflection features being assigned to the same second reflection feature, and vice versa.

[0069] Each of the first and second reflective features includes at least one beam profile. As used herein, the term "beam profile" may generally refer to a spatial distribution of the intensity of a light beam, particularly in at least one plane perpendicular to the propagation of the light beam. The beam profile may be a transverse intensity profile of the light beam. The beam profile may be a cross-section of the light beam. The beam profile may be selected from the group consisting of a trapezoidal beam profile, a triangular beam profile, a conical beam profile, and a linear combination of a Gaussian beam profile. However, other embodiments are possible. The beam profile may also be referred to as a reflective profile.

[0070] The evaluation device may be configured to determine an ordinate for each of the first reflective features by analysis of the respective beam profiles. The analysis of the beam profiles may include determining at least one first area and at least one second area of ​​the beam profiles. The evaluation device may be configured to derive the combined signal Q by one or more of dividing the first area and the second area, dividing a multiple of the first area and the second area, dividing a linear combination of the first area and the second area. The evaluation device may be configured to use at least one predetermined relationship between the combined signal Q and the ordinate to determine the ordinate.

[0071] The evaluation device determines, for each of the first reflective features, z DPRThe evaluation device may be configured to determine at least one ordinate, also referred to as the ordinate. As used herein, the term "analysis of the beam profile" may generally refer to the evaluation of the beam profile and may include at least one mathematical operation, and / or at least one comparison, and / or at least one symmetrization, and / or at least one filtering, and / or at least one normalization. For example, the analysis of the beam profile may include at least one of the following: a histogram analysis step, a calculation of a difference measure, application of a neural network, application of a machine learning algorithm. The evaluation device may be configured to symmetrize and / or normalize and / or filter the beam profile, in particular to remove noise or asymmetry from recordings at larger angles, recordings of edges, etc. The evaluation device may filter the beam profile by removing high spatial frequencies, such as by spatial frequency analysis and / or median filtering. The aggregation may be performed by center of intensity of the light spot and averaging of all intensities at the same distance to the center. The evaluation device may be configured to normalize the beam profile to a maximum intensity, in particular to take into account intensity differences due to the recorded distance. The evaluation device may be configured to remove the influence of background light from the beam profile, for example by imaging without illumination.

[0072] The reflection feature may cover at least one pixel of the image or extend over at least one image. For example, the reflection feature may cover or extend over multiple pixels. The evaluation device may be configured to determine and / or select all pixels that are connected to and / or belong to a reflection feature, e.g. a light spot. The evaluation device may determine the center of intensity as

number

[0073] The evaluation device may be configured to determine an ordinate for each of the first reflective features by using a Depth Ratio from Photons technique, also called beam profile analysis. For Depth Ratio from Photons (DPR) techniques, see WO2018 / 091649A1, WO2018 / 091638A1, and WO2018 / 091640A1, and C. Lennartz, F. Schick, S. Metz, "Whitepaper-Beam Profile Analysis for 3D imaging and material detection", April 28, 2021, Ludwigshafen, Germany, the entire contents of which are incorporated by reference.

[0074] The evaluation device may be configured to determine a beam profile for each of the first reflection features. As used herein, the term "determine a beam profile" refers to identifying and / or selecting at least one first reflection feature in the first reflection image and evaluating at least one intensity distribution of the first reflection feature. By way of example, a region of the first reflection image may be used and evaluated to determine an intensity distribution, such as a three-dimensional intensity distribution or a two-dimensional intensity distribution, such as along an axis or line through the first reflection image. By way of example, the center of illumination by the light beam may be determined, such as by determining at least one pixel having the highest illumination, and a cross-sectional axis passing through the center of illumination may be selected. The intensity distribution may be an intensity distribution as a function of coordinate along this cross-sectional axis passing through the center of illumination. Other evaluation algorithms are possible.

[0075] The analysis of the beam profile of one of the first reflection features may include determining at least one first area and at least one second area of ​​the beam profile. The first area of ​​the beam profile may be area A1, and the second area of ​​the beam profile may be area A2. The evaluation device may be configured to integrate the first area and the second area. The evaluation device may be configured to derive a combined signal Q, also referred to as a quotient Q, by one or more of: dividing the integrated first area and the integrated second area; dividing a multiple of the integrated first area and the integrated second area; dividing a linear combination of the integrated first area and the integrated second area.

[0076] The evaluation device may be configured to determine at least two areas of the beam profile and / or to divide the beam profile into at least two segments including different areas of the beam profile, where overlap of the areas may be possible as long as the areas do not coincide. For example, the evaluation device may be configured to determine a plurality of areas, such as two, three, four, five, or up to ten areas. The evaluation device may be configured to divide the light spot into at least two areas of the beam profile and / or to divide the beam profile into at least two segments including different areas of the beam profile. The evaluation device may be configured to determine, for at least two of the areas, an integral of the beam profile over the respective areas. The evaluation device may be configured to compare at least two of the determined integrals. In particular, the evaluation device may be configured to determine at least one first area and at least one second area of ​​the beam profile. As used herein, the term "area of ​​the beam profile" generally refers to any region of the beam profile at the location of the optical sensor used to determine the combined signal. The first area of ​​the beam profile and the second area of ​​the beam profile may be adjacent or overlapping or both. The first area of ​​the beam profile and the second area of ​​the beam profile may not coincide in area. For example, the evaluation device may be configured to divide the sensor area of ​​the first sensor element into at least two sub-areas, and the evaluation device may be configured to divide the sensor area of ​​the first sensor element into at least one left part and at least one right part, and / or at least one upper part and at least one lower part, and / or at least one inner part and at least one outer part. The evaluation device may be adapted to integrate the first area and the second area.

[0077] The first area of ​​the beam profile may essentially include edge information of the beam profile, the second area of ​​the beam profile may essentially include center information of the beam profile, and / or the first area of ​​the beam profile may essentially include information about the left part of the beam profile and the second area of ​​the beam profile may essentially include information about the right part of the beam profile. The beam profile may have a center, i.e. a maximum of the beam profile and / or a center point of the plateau of the beam profile and / or a geometric center of the light spot, and a falling edge extending from the center. The second region may include an inner region of the cross section and the first region may include an outer region of the cross section. As used herein, the term "essentially center information" generally refers to a low percentage of edge information, i.e. a low percentage of the intensity distribution corresponding to the edge, compared to the percentage of center information, i.e. a percentage of the intensity distribution corresponding to the center. Preferably, the center information has a percentage of edge information less than 10%, more preferably less than 5%, and most preferably the center information does not include edge content. As used herein, the term "essentially edge information" generally refers to a low percentage of center information, compared to the percentage of edge information. The edge information may include information of the entire beam profile, especially information from the central and edge regions. The edge information has a percentage of central information less than 10%, preferably less than 5%, and more preferably the edge information does not include central content. At least one area of ​​the beam profile may be determined and / or selected as the second area of ​​the beam profile if the area is close to or around the center and essentially includes central information. At least one area of ​​the beam profile may be determined and / or selected as the first area of ​​the beam profile if the area includes at least a portion of the falling edge of the cross section. For example, the entire area of ​​the cross section may be determined as the first area.

[0078] Other selections of the first area A1 and the second area A2 may be feasible. For example, the first area may essentially include the outer region of the beam profile, and the second area may essentially include the inner region of the beam profile. For example, in the case of a two-dimensional beam profile, the beam profile may be divided into a left part and a right part, and the first area may essentially include the area of ​​the left part of the beam profile, and the second area may essentially include the area of ​​the right part of the beam profile.

[0079] The edge information may include information about the number of photons in a first area of ​​the beam profile, and the center information may include information about the number of photons in a second area of ​​the beam profile. The evaluation device may be configured to determine an area integral of the beam profile. The evaluation device may be configured to determine the edge information by integration and / or summation of the first area. The evaluation device may be configured to determine the center information by integration and / or summation of the second area. For example, the beam profile may be a trapezoidal beam profile and the evaluation device may be configured to determine the integral of the trapezoid. Furthermore, if a trapezoidal beam profile can be assumed, the determination of the edge and center signals can be replaced by an equivalent evaluation utilizing the properties of the trapezoidal beam profile, such as determining the slope and position of the edges and the height of the central plateau, and deriving the edge and center signals by geometrical considerations.

[0080] In one embodiment, A1 may correspond to the total area or complete area of ​​the feature point. A2 may be the central area of ​​the feature point. The central area may be a constant value. The central area may be smaller compared to the total area of ​​the feature point. For example, for a circular feature point, the central area may have a radius between 0.1 and 0.9 of the total radius of the feature point, preferably between 0.4 and 0.6 of the total radius.

[0081] In one embodiment, the illumination pattern may include at least a point pattern. A1 may correspond to an area having a full radius of the points of the point pattern on the optical sensor. A2 may be a central area of ​​the points of the point pattern on the optical sensor. The central area may be a constant value. The central area may have a radius compared to the full radius. For example, the central area may have a radius between 0.1 and 0.9 of the full radius, preferably between 0.4 and 0.6 of the full radius.

[0082] The evaluation device may be configured to derive the quotient Q by one or more of: dividing the first area and the second area; dividing a multiple of the first area and the second area; dividing a linear combination of the first area and the second area.

number

[0083] Additionally or alternatively, the evaluation device may be adapted to determine one or both of the center information and edge information from at least one slice or cut of the light spot. This may be achieved, for example, by replacing the area integral of the quotient Q with a line integral along the slice or cut. To improve accuracy, several slices or cuts through the light spot may be used and averaged. In case of an elliptical spot profile, averaging over several slices or cuts provides improved distance information.

[0084] For example, the evaluation device - determining the pixel having the highest sensor signal and forming at least one central signal; - evaluating the sensor signals of the matrix and forming at least one sum signal; - determining a quotient Q by combining the center signal and the sum signal; - determining at least one ordinate z of the object by evaluating the quotient Q; The beam profile may be evaluated by:

[0085] The sensor signal may be a signal generated by the optical sensor and / or at least one pixel of the optical sensor in response to illumination. In particular, the sensor signal may be or include at least one electrical signal, such as at least one analog electrical signal and / or at least one digital electrical signal. More particularly, the sensor signal may be or include at least one voltage signal and / or at least one current signal. More particularly, the sensor signal may include at least one photocurrent. Furthermore, raw sensor signals may be used, or the detector, optical sensor, or other elements may be adapted to process or pre-process the sensor signal, such as pre-processing by filtering, thereby generating a secondary sensor signal that may also be used as a sensor signal. The term "center signal" generally refers to at least one sensor signal that essentially includes center information of the beam profile. As used herein, the term "highest sensor signal" refers to one or both of a local maximum or a maximum within a region of interest. For example, the central signal may be the signal of a pixel having the highest sensor signal of a plurality of sensor signals generated by the pixels of the entire matrix or of a region of interest in the matrix, the region of interest being predefined or determinable in the image generated by the pixels of the matrix. The central signal may originate from a single pixel or from a group of optical sensors, in the latter case, by way of example, the sensor signals of the group of pixels may be added, integrated or averaged to determine the central signal. The group of pixels from which the central signal occurs may be a group of adjacent pixels, such as pixels having a distance less than a predefined distance from the actual pixel with the highest sensor signal, or a group of pixels generating a sensor signal that is within a predefined range from the highest sensor signal. The group of pixels from which the central signal occurs may be selected as large as possible to allow a maximum dynamic range. The evaluation device may be adapted to determine the central signal by integrating a plurality of sensor signals, for example a plurality of pixels around the pixel with the highest sensor signal. For example, the beam profile may be a trapezoidal beam profile, and the evaluation device may be adapted to determine the integral of the trapezoid, in particular the integral of the plateau of the trapezoid.

[0086] As mentioned above, the central signal may generally be a single sensor signal, such as a sensor signal from a pixel at the center of the light spot, or may be a combination of multiple sensor signals, such as a combination of sensor signals originating from pixels at the center of the light spot, or may be a secondary sensor signal derived by processing sensor signals derived by one or more of the above possibilities. The determination of the central signal may be performed electronically, as comparison of sensor signals is fairly easily performed by conventional electronics, or may be performed fully or partially by software. Specifically, the center signal may be selected from the group consisting of the highest sensor signal, the average of a group of sensor signals that are within a predetermined tolerance range of the highest sensor signal, the average of sensor signals from a group of pixels including the pixel with the highest sensor signal and a predetermined group of adjacent pixels, the sum of sensor signals from a group of pixels including the pixel with the highest sensor signal and a predetermined group of adjacent pixels, the sum of a group of sensor signals that are within a predetermined tolerance range of the highest sensor signal, the average of a group of sensor signals that exceed a predetermined threshold, the sum of a group of sensor signals that exceed a predetermined threshold, an integral of a group of sensor signals from a group of optical sensors including the optical sensor with the highest sensor signal and a predetermined group of adjacent pixels, an integral of a group of sensor signals that are within a predetermined tolerance range of the highest sensor signal, and an integral of a group of sensor signals that exceed a predetermined threshold.

[0087] Similarly, the term "sum signal" generally refers to a signal that essentially contains edge information of the beam profile. For example, the sum signal may be derived by adding, integrating or averaging the sensor signals of the entire matrix or of a region of interest in the matrix, the region of interest being predefined or determinable in the image generated by the optical sensor of the matrix. When adding, integrating or averaging the sensor signals, the actual optical sensor where the sensor signal is generated may be excluded from the summation, integration or averaging or may be included in the summation, integration or averaging. The evaluation device may be adapted to determine the sum signal by integrating the signals of the entire matrix or of a region of interest in the matrix. For example, the beam profile may be a trapezoidal beam profile and the evaluation device may be adapted to determine the integral of the entire trapezoid. Furthermore, if a trapezoidal beam profile can be assumed, the determination of the edge and center signals may be replaced by equivalent evaluations that exploit the properties of the trapezoidal beam profile, such as determining the slope and position of the edges and the height of the central plateau, and deriving the edge and center signals by geometrical considerations.

[0088] Similarly, the center signal and the edge signal can be determined by using a segment of the beam profile, such as a circular segment of the beam profile. For example, the beam profile can be divided into two segments by a secant or chord that does not pass through the center of the beam profile. Thus, one segment essentially contains edge information and the other segment essentially contains center information. For example, the edge signal can be further subtracted from the center signal to further reduce the amount of edge information in the center signal.

[0089] The combined signal Q, also called quotient Q, may be a signal generated by combining the center signal and the sum signal. In particular, the determination may include one or more of forming a quotient of the center signal and the sum signal or its inverse, forming a quotient of a multiple of the center signal and a multiple of the sum signal or its inverse, forming a quotient of a linear combination of the center signals and a linear combination of the sum signal or its inverse. Additionally or alternatively, the quotient Q may include any signal or combination of signals that includes at least one item of information regarding the comparison of the center signal and the sum signal.

[0090] As used herein, the term "ordinate of the first reflection feature" may refer to the distance between the first camera and the object. The evaluation device may be configured to use at least one predetermined relationship between the combined signal and the ordinate to determine the ordinate. The predetermined relationship may be one or more of an empirical relationship, a semi-empirical relationship, and an analytically derived relationship. The evaluation device may include at least one data storage device for storing the predetermined relationship, such as a look-up list or a look-up table.

[0091] The evaluation device may be configured to execute a depth ratio algorithm from at least one photon that calculates distances for all first reflection features of zeroth order and higher orders.

[0092] The evaluation device may be configured to match the first and second reflection characteristics by using the determined ordinate.

[0093] The evaluation device may be configured to determine a longitudinal area for each of the first reflection features, the longitudinal area being given by the ordinate of the reflection feature and an error interval ±ε.

[0094] The evaluation device may be adapted to identify at least one second reflection feature in the second reflection image that has essentially the same ordinate as the selected first reflection feature. The term "essentially the same" refers to being identical within 10%, preferably within 5%, most preferably within 1%. The second reflection feature corresponding to the first reflection feature may be determined using epipolar geometry. For a description of epipolar geometry, see for example X. Jiang, H. Bunke, Chapter 2: "Dreidimensionales Computersehen", Springer, Berlin Heidelberg, 1997. In epipolar geometry, it may be assumed that the first reflection image and the second reflection image are images of an object determined at different spatial positions and / or spatial orientations with a fixed distance. The evaluation device may be adapted to determine an epipolar line in the second reflection image. The baseline of the first reflection image (of the first camera) and the second reflection image (of the second camera) may be known. For example, the relative positions of the first and second reflected images may be stored in at least one storage unit of the evaluation device. The evaluation device may be adapted to determine a straight line extending from the selected first reflected feature. The straight line may include the possible object feature corresponding to the selected first reflected feature. The straight line and the baseline span the epipolar plane. Since the second reflected image is determined at a different relative position than the first reflected image, the corresponding possible object feature may be imaged on a straight line, called an epipolar line, in the second reflected image. Thus, the feature of the second reflected image corresponding to the selected first reflected feature lies on the epipolar line.

[0095] The evaluation device may be configured to determine at least one displacement region in the second reflected image corresponding to the longitudinal region. As used herein, the term "displacement region" refers to a region in the second reflected image in which a second reflection feature corresponding to the selected first reflection feature may be imaged. Specifically, the displacement region may be a region in the second reflected image in which a second reflection feature corresponding to the selected first reflection feature is expected to be located in the second reflected image. Depending on the distance to the object, the image position of the second reflection feature corresponding to the first reflection feature may be displaced in the second reflected image compared to the image position of the first reflection feature in the first reflected image. The displacement region may include only one second reflection feature. The displacement region may also include more than one second reflection feature. The displacement region may include an epipolar line or a portion of an epipolar line. The displacement region may include more than one epipolar line or more portions of more than one epipolar line. The displacement area may extend along the epipolar line, or may be orthogonal to the epipolar line, or both. The evaluation device may be adapted to determine a second reflection feature along the epipolar line corresponding to the distance feature and to determine a range of the displacement area along the epipolar line or orthogonal to the epipolar line corresponding to the error interval ±ε. The measurement uncertainty of the distance estimation may give rise to a non-circular displacement area since the measurement uncertainty may be different for different directions. In particular, the measurement uncertainty along one or more epipolar lines may be larger than the measurement uncertainty in a direction orthogonal to the one or more epipolar lines. The displacement area may include an extension in a direction orthogonal to the one or more epipolar lines.

[0096] The evaluation device may be configured to match one of the first reflection features to one of the second reflection features in the displacement regions, respectively. The evaluation device may be configured to match the first reflection feature to the second reflection feature in the respective displacement region by using at least one evaluation algorithm taking into account the determined ordinate. The evaluation algorithm may be a linear scaling algorithm.

[0097] The evaluation device may be configured to determine a displacement of the matched first and second reflection features. The displacement may be a difference between an image position of the first reflection feature in the first reflection image and an image position of the second reflection feature in the second reflection image. The image positions may be pixel coordinates in the respective reflection images. The evaluation device may be configured to determine the refined ordinates by triangulation using a predetermined relationship between the ordinates and the displacement. By using triangulation with a second camera having a higher baseline, a high degree of accuracy is obtained.

[0098] The predetermined relationship may be one or more of an empirical relationship, a semi-empirical relationship, and an analytically derived relationship. The evaluation device may include at least one data storage device for storing the predetermined relationship, such as a look-up list or a look-up table. The evaluation device may be adapted to determine the predetermined relationship by using a triangulation method. If the position of the selected first reflection feature in the first reflection image and the position of the matched second reflection feature and / or the relative displacement of the matched first reflection feature and the second reflection feature are known, the refined ordinate may be determined by triangulation. Thus, the evaluation device may be adapted to select the first reflection feature, for example consecutively and / or row by row, and to determine, for each potential position of the second reflection feature, a corresponding distance value using triangulation. The displacement and the corresponding distance value may be stored in at least one storage device of the evaluation device.

[0099] Additionally or alternatively, the evaluation device may include the following steps: - determining a displacement region for an image location of each first reflective feature; - assigning an epipolar line to the displacement region of each first reflection feature, such as by assigning an epipolar line closest to the displacement region and / or an epipolar line within the displacement region and / or an epipolar line closest to the displacement region along a direction orthogonal to the epipolar line; - assigning and / or determining at least one second reflection feature to each first reflection feature, such as by assigning a second reflection feature closest to the assigned displacement area and / or a second reflection feature within the assigned displacement area and / or a second reflection feature closest to the assigned displacement area along the assigned epipolar line and / or a second reflection feature within the assigned displacement area along the assigned epipolar line; The method may be configured to perform the following steps:

[0100] Additionally or alternatively, the evaluation device may be adapted to discriminate between two or more epipolar lines and / or second reflection features assigned to a first reflection feature, such as by comparing distances of the epipolar lines in the first reflection feature and / or the second reflection image and / or by comparing error-weighted distances, such as ε-weighted distances, of the epipolar lines in the first reflection feature and / or the second reflection image and assigning the epipolar line and / or second reflection feature with the shorter distance and / or ε-weighted distance to the second reflection feature and / or the first reflection feature.

[0101] The evaluation device is configured to determine a combined material property of the matched pair of first and second reflection features by analyzing the beam profile. As used herein, the term "combined material property" is a broad term and indicates the usual and customary meaning for a person skilled in the art, and is not limited to a special or special meaning. The term may specifically, but not limited to, refer to a material property determined by using and / or taking into account information from the first and second reflection features. The determination of the combined material property may include determining a material property for each of the first and second reflection features and combining the determined material property with the combined material property. This may allow to reduce the measurement uncertainty. The projector may project an illumination feature onto the object. Both cameras may image the object with the projected illumination features. The beam profile of the first and second reflection features may be evaluated, which may allow to improve the quality of the determination of the material property. The beam profile analysis may allow to extract additional material information of the object. This configuration using two cameras may improve material detection, especially with respect to robustness. Two different views of an illuminated feature on an object can provide more information of the material scattering properties. By using two views, the sample size is twice as large, which may allow for forced feature-based material detection. Due to the material roughness due to speckle noise, backscattered light intensity, sub-scattering properties of the material, and noise reduction by increasing the sample size, a more robust material determination may be possible. Using two views may be particularly advantageous for highly reflective materials such as plastic foils.

[0102] The determination of the material properties of each reflection feature may be performed using beam profile analysis. In particular, the beam profile analysis utilizes the reflection properties of the coherent light projected on the object surface to classify the material. The material classification may be performed as described in one of WO2020 / 187719, WO2021 / 170791A1, and / or WO2021 / 152070, the entire contents of which are incorporated by reference. In particular, the analysis of the beam profile of each reflection feature recorded by the camera may be performed by a feature-based method. The feature-based method may be described below. Additionally or alternatively, machine learning methods may be used. The feature-based method may be used in combination with the machine learning method to allow parameterization of the skin classification model. Alternatively or in combination, a convolutional neuron network may be used to classify the skin by using the reflection image as input.

[0103] For example, the evaluation device may be configured to identify the reflection feature as being generated by illuminating biological tissue, in particular human skin, if the reflected beam profile meets at least one predetermined or predefined criterion. As used herein, the term "at least one predetermined or predefined criterion" refers to at least one characteristic and / or value suitable for distinguishing biological tissue, in particular human skin, from other materials. The predetermined or predefined criterion may be or include at least one predetermined or predefined value and / or threshold and / or threshold range referring to a material characteristic. If the reflected beam profile meets at least one predetermined or predefined criterion, the reflection feature may be indicated as being generated by biological tissue. The evaluation device may be configured to identify the reflection feature as non-skin in other cases. As used herein, the term "biological tissue" generally refers to biological materials that include living cells. In particular, the evaluation device may be configured for skin detection. The term "identification" of what is produced by living tissue, in particular human skin, may refer to determining and / or verifying whether the surface to be examined or under test is or comprises living tissue, in particular human skin, and / or distinguishing living tissue, in particular human skin, from other tissues, in particular other surfaces. The invention may enable distinguishing human skin from one or more of inorganic tissues, metal surfaces, plastic surfaces, foams, paper, wood, displays, screens, fabrics. The invention may enable distinguishing human living tissue from surfaces of artificial objects or inanimate surfaces.

[0104] The reflectance properties of skin can be characterized by the simultaneous occurrence of direct reflection at the surface (Lambertian) and subsurface scattering (volume scattering). This allows the laser spot on the skin to be broadened in comparison to the above-mentioned materials. For example, without wishing to be bound by theory, an object, such as human skin, can have a reflectance profile, also called a backscattering profile. The reflectance profile can include a portion generated by the back reflection of the surface, called the surface reflection, and a portion generated by the very diffuse reflection from the light penetrating the skin, called the diffuse portion of the back reflection. For the reflectance profile of human skin, see "Lasertechnik in der Medizin: Grundlagen, Systeme, Anwendungen", "Wirkung von Laserstrahlung auf Gewebe", 1991, pp. 171-266, Juergen Eichler, Theo Seiler, Springer Verlag, ISBN 0939-0979. The surface reflectance of the skin can increase as the wavelength increases towards the near infrared. Furthermore, the penetration depth may increase as the wavelength increases from visible to near infrared. The diffuse portion of the retroreflection may increase with the penetration depth of the light. These properties may be used to distinguish skin from other materials by analyzing the retroscattering profile.

[0105] In particular, the evaluation device may be configured to compare the reflected beam profile with at least one predefined and / or prerecorded and / or predefined beam profile. The predefined and / or prerecorded and / or predefined beam profile may be stored in a table or a look-up table, may be determined, for example empirically, and may be stored, for example, in at least one data storage device of the detector. For example, the predefined and / or prerecorded and / or predefined beam profile may be determined during initial start-up of the device performing the method according to the invention. For example, the predefined and / or prerecorded and / or predefined beam profile may be stored in at least one data storage device of the evaluation device, for example by software, in particular by an app downloaded from an app store or the like. The reflection feature may be identified as being generated by biological tissue if the reflected beam profile and the predefined and / or prerecorded and / or predefined beam profile are identical. The comparison may include superimposing the reflected beam profile and the predefined or predefined beam profile such that their centers of intensity coincide. The comparison may comprise determining a deviation, e.g. a sum of squares of point-to-point distances, between the reflected beam profile and a pre-determined and / or pre-recorded and / or pre-defined beam profile. The evaluation device may be adapted to compare the determined deviation with at least one threshold value, and if the determined deviation is less than and / or equal to the threshold value, the surface is indicated as biological tissue and / or the detection of biological tissue is confirmed. The threshold value may be stored in a table or a look-up table, may be determined, e.g. empirically, and may be stored, for example, in at least one data storage device of the evaluation device.

[0106] Additionally or alternatively, material properties may be determined by applying at least one image filter to an image of a reflective feature. As further used herein, the term "image" refers to a two-dimensional function f(x,y), where brightness and / or luminosity are given for any x,y position in the image. The positions may be discretized corresponding to the recording pixels. The brightness and / or color may be discretized corresponding to the bit depth of the optical sensor. As used herein, the term "image filter" refers to at least one mathematical operation applied to the beam profile and / or to at least one specific region of the beam profile. In particular, the image filter Φ maps the image f or a region of interest in the image to a real number Φ(f(x,y)) = φ, where φ denotes a feature, in particular a material feature. Images may be subject to noise, as may features. Thus, features may be random variables. Features may be normally distributed. If the features are not normally distributed, they may be transformed to be normally distributed, such as by a Box-Cox transformation.

[0107] The evaluation device determines at least one material characteristic φ by applying at least one material-dependent image filter Φ2 to the image. 2m As used herein, the term "material dependent" image filter refers to an image having a material dependent output. The output of a material dependent image filter is referred to herein as a "material feature φ 2m " or "Material-dependent feature φ 2m The material feature may be or may include at least one piece of information regarding at least one material property of the surface in the scene that generated the reflectance feature.

[0108] The material dependent image filter may be a brightness filter, a spot shape filter, a squared norm gradient, a standard deviation, a smoothness filter such as a Gaussian filter or a median filter, a gray level generation based contrast filter, a gray level generation based energy filter, a gray level generation based uniformity filter, a gray level generation based dissimilarity filter, a rho energy filter, a threshold domain filter, or a linear combination thereof; or a brightness filter, a spot shape filter, a squared norm gradient, a standard deviation, a smoothness filter, a gray level generation based energy filter, a gray level generation based uniformity filter, a gray level generation based dissimilarity filter, a rho energy filter, or a threshold domain filter, or |ρ Φ2other,Φm Further material-dependent image filters Φ correlated to one or more of their linear combinations by |≧0.40 2other and at least one filter selected from the group consisting of: m is one of the following: a brightness filter, a spot shape filter, a squared norm gradient, a standard deviation, a smoothness filter, a gray level generation based energy filter, a gray level generation based uniformity filter, a gray level generation based dissimilarity filter, a low energy filter, or a threshold domain filter, or a linear combination thereof. 2other is the material-dependent image filter Φ m and |ρ Φ2other,Φm |≧0.60, preferably |ρ Φ2other,Φm may be correlated by |≧0.80.

[0109] The material-dependent image filter may be at least one any filter Φ that passes hypothesis validation. As used herein, the term “passes hypothesis validation” refers to the fact that the null hypothesis H0 is rejected and an alternative hypothesis H1 is accepted. The hypothesis validation may include validating the material dependency of the image filter by applying the image filter to a predefined data set. The data set may include multiple beam profile images. As used herein, the term “beam profile image” refers to a set of NB It refers to the sum of Gaussian radial basis functions,

number

[0110]

number

[0111] [Table 1]

[0112] The values ​​of x and y are

number

[0113] Next, for each image f k For , the feature value corresponding to the filter Φ

number

number

number

number

number

number

number

[0114] Hypothesis testing is

number

[0115] Hypothesis testing may include performing an F-test.

[0116]

number

[0117] Here, d1=NJ, d2=J-1 F(x)=1-CDF(x) p = F(mssb / mssw)

[0118] Here, I x is the regularized incomplete β function

number

number

number

[0119] In the following, the image filter is described assuming that the reflection image contains at least one reflection feature, in particular a spot image, f, which is a function

number

[0120] For example, the material dependent image filter may be a luminance filter. The luminance filter may return a luminance measurement of a spot as a material characteristic. The material characteristic may be:

number

number

number

[0121] For example, a material dependent image filter may be a filter that has an output that is dependent on the spot shape. This material dependent image filter may return a value as a material characteristic that correlates to the translucency of the material. The translucency of the material affects the shape of the spot. The material characteristic may be:

number

number

[0122] For example, the material dependent image filter can be a squared norm gradient, which can return a value correlating to a measure of the soft-hard transition and / or roughness of the spot as a material characteristic.

number

[0123] For example, the material dependent image filter can be the standard deviation of the spot.

number

[0124] For example, the material dependent image filter may be a smoothness filter, such as a Gaussian filter or a median filter. In one embodiment of the smoothness filter, the image filter may refer to the observation that volume scattering exhibits less speckle contrast compared to diffuse scattering materials. The image filter may quantify the smoothness of the spots corresponding to the speckle contrast as a material feature. The material feature may be:

number

number

[0125] The material characteristics of this filter are:

number

[0126] For example, the image filter can be a gray level generation based contrast filter. This material filter is based on the gray level generation matrix M f,ρ (g1, g2) = [P g1,g2 ], while P g1,g2 is the occurrence rate of the grey combination (g1,g2)=[f(x1,y1),f(x2,y2)], the relation ρ defines the distance between (x1,y1) and (x2,y2), and ρ(x,y)=(x+a,y+b), where a and b are selected from 0,1.

[0127] The material characteristics of the gray level generation based contrast filter are:

number

[0128] For example, the image filter can be a gray level generation based energy filter, which is based on the gray level generation matrix described above.

[0129] The material characteristics of the gray level generation based energy filter are:

number

[0130] For example, the image filter can be a gray level generation based uniformity filter. The material filter is based on the gray level generation matrix above. The material characteristics of the gray level generation based uniformity filter are:

number

[0131] For example, the image filter can be a gray level generation based dissimilarity filter. The material filter is based on the above gray level generation matrix. The material feature of the gray level generation based dissimilarity filter is:

number

[0132] For example, the image filter can be a low energy filter. The material filter is a low vector L5=[1,4,6,4,1] and E5=[-1,-2,0,-2,-1] and a matrix L5(E5) T and E5 (L5) T Image f k are these matrices

number

number

[0133] On the other hand, the material characteristics of the low energy filter are:

number

[0134] For example, the material dependent image filter may be a threshold domain filter. This material feature may relate two areas in the image plane. The first area Ω1 may be the area where the function f is greater than α times the maximum value of f. The second area Ω2 may be the area where the function f is less than α times the maximum value of f but greater than a threshold ε times the maximum value of f. Preferably, α may be 0.5 and ε may be 0.05. Due to speckle or noise, the areas may not simply correspond to an inner and outer circle around the spot center. As an example, Ω1 may include speckle or a disconnected area of ​​the outer circle. The material feature may be:

number

[0135] The evaluation device measures the material characteristic φ to determine the material properties of the surface that generated the reflection characteristic. 2m and a material property of the surface that produced the reflectance feature. The predetermined relationship may be one or more of an empirical relationship, a semi-empirical relationship, and an analytically derived relationship. The evaluation device may include at least one data store for storing the predetermined relationships, such as a look-up list or a look-up table.

[0136] Additionally or alternatively, the material properties of each of the reflection features may be implemented using artificial intelligence, in particular a convolutional neuron network. By using the reflection image as the input of a convolutional neuron network, it may be possible to generate a classification model with sufficient accuracy to distinguish between materials, for example between skin and other volumetric scattering materials. By selecting important regions in the reflection image, only physically valid information is passed to the network, so that only a compact training data set may be required. In addition, a very compact network architecture may be generated.

[0137] In particular, at least one parameterized classification model may be used. The parameterized classification model may be configured to classify the materials by using the reflectance image as an input. The classification model may be parameterized by using one or more of machine learning, deep learning, neural networks, or other forms of artificial intelligence. As used herein, the term "machine learning" is a broad term and is intended to have a normal and customary meaning for those skilled in the art, and is not limited to a specific or special meaning. The term may in particular, but is not limited to, a method of using artificial intelligence (AI) for automatic model building, in particular for model parameterization. The term "classification model" may refer to a model configured to distinguish a material, for example human skin, from other materials. The characteristic properties of each material may be determined by applying an optimization algorithm with respect to at least one optimization target of the classification model. The machine learning may be based on at least one neuronal network, in particular a convolutional neural network. The weights and / or topology of the neuronal network may be predetermined and / or predefined. In particular, the training of the classification model may be performed using machine learning. The classification model may include at least one machine learning architecture and model parameters. For example, the machine learning architecture may be or include one or more of linear regression, logistic regression, random forest, naive Bayes classification, nearest neighbor, neural network, convolutional neural network, generative adversarial network, support vector machine, or gradient boosting algorithm, etc. As used herein, the term "training", also referred to as learning, is a broad term and refers to its ordinary and customary meaning to those skilled in the art, and is not limited to any special or special meaning. The term may specifically refer to, but is not limited to, methods of building a classification model, and in particular methods of determining and / or updating parameters of the classification model. The classification model may be at least partially data-driven.For example, the skin classification model may be based on empirical data, such as data determined by illuminating a number of humans and man-made objects, such as masks, and recording the reflectance patterns. For example, training may include using at least one training data set, the training data set including images, particularly reflectance images, of a number of humans and man-made objects having known material properties.

[0138] Additionally or alternatively, the material properties may be determined using a bidirectional reflectance distribution function of the matched first and second reflectance features. The evaluation device may be configured to determine the bidirectional reflectance distribution function of the matched first and second reflectance features. The evaluation device may be configured to determine the material properties by evaluating the bidirectional reflectance distribution function. As used herein, the term "bidirectional reflectance distribution function (BRDF)" is a broad term and is intended to have a normal and customary meaning to those skilled in the art and is not limited to a special or special meaning. The term may specifically refer to, but is not limited to, a luminance distribution as a function of viewing angle. The BRDF may be a combined beam profile determined by using two different views on each illumination feature. The material properties may be modeled by a bidirectional reflectance distribution function. For example, the actual appearance of a spatially uniform material may be represented by a BRDF, as described in J. Filip et al., "BRDF Measurement of Highly-Specular Materials using a Goniometer," a preprint submitted to SCCG 2017 in April 2017. The BRDF defines how light is reflected by the surface of a material in the illumination direction ω. i = {θ, φ i} and line of sight ω v = {θ v , φ v} a 4-dimensional vector-valued function f r (θ i , θ v , φ i , φ v), where θ∈[0,π / 2] is the elevation angle and φ ∈ [0,2π] is the azimuth angle in a spherical coordinate system. Two views of an illuminated feature on an object may allow sampling of the BRDF.

[0139] The evaluation of the bidirectional reflectance distribution function may comprise comparing the bidirectional reflectance distribution function with at least one predefined bidirectional reflectance distribution function, which may be stored in a table or a look-up table, may be determined, for example, empirically, and may be stored in at least one data store of the evaluation device, for example, by software, in particular by an app downloaded from an app store or the like.

[0140] The reflectance feature may be identified as being produced by a material, e.g. biological tissue, if the bidirectional reflectance distribution function and a predefined bidirectional reflectance distribution function are identical, at least within a tolerance. The evaluation of the bidirectional reflectance distribution function may include comparing the bidirectional reflectance distribution function with at least one predefined bidirectional reflectance distribution function. The comparison may include superimposing the bidirectional reflectance distribution function and the predefined bidirectional reflectance distribution function. The comparison may include determining a deviation, e.g. a sum of squared point-to-point distances, between the bidirectional reflectance distribution function and the predefined bidirectional reflectance distribution function. The evaluation device may be adapted to compare the determined deviation with at least one threshold value, and if the determined deviation is less than and / or equal to the threshold value, the surface is indicated as a particular material, e.g. biological tissue. The threshold value may be stored in a table or a look-up table, may be determined, e.g. empirically, and may be stored, for example, in at least one data storage device of the evaluation device.

[0141] The detector may be configured to automatically calibrate each of the first and second cameras to the projector and / or to automatically calibrate the first and second cameras to each other. With regard to the calibration of each of the first and second cameras to the projector, reference is made to European Patent Application No. 21207250.8, filed on November 9, 2021, the entire contents of which are incorporated by reference.

[0142] In a further aspect of the invention, a method according to the invention for determining material properties of at least one object using at least one detector for identifying at least one material property is disclosed. With regard to the definition and embodiments of the method, reference is made to the description of the detector as described in the first aspect of the invention.

[0143] The method steps may be performed in a given order or in a different order. Further, there may be one or more additional method steps not listed. Further, one, more than one, or even all of the method steps may be performed repetitively.

[0144] The method comprises the following steps: a) illuminating an object with at least one illumination pattern including a plurality of illumination features by using a projector; b) capturing at least one first reflected image comprising a plurality of first reflective features produced by the object in response to illumination by the illumination features by using a first camera, the first camera being arranged such that the first reflected image is captured in a first line of sight direction relative to the object; c) capturing at least one second reflected image comprising a plurality of second reflected features produced by the object in response to illumination by the illumination features by using a second camera, the second camera being arranged such that the second reflected image is captured at a second line of sight direction relative to the object, the second line of sight direction being different from the first line of sight direction and the second line of sight direction; d) evaluating the first and second reflection images by using an evaluation device, the evaluation including matching the first and second reflection features and determining combined material properties of the matched first and second reflection feature pairs by analysis of their beam profiles.

[0145] The method may be computer-implemented. As used herein, the term "computer-implemented" is a broad term and is intended to have its ordinary and customary meaning to those skilled in the art and is not limited to any specific or special meaning. The term specifically refers to a method that is fully or partially implemented using a data processing means, such as, but not limited to, a data processing means including at least one processing unit.

[0146] Further disclosed and proposed herein is a computer program comprising computer executable instructions for carrying out the method according to the invention in one or more of the embodiments contained herein, when the program is executed on a computer or a computer network. In particular, the computer program may be stored on a computer readable data carrier and / or a computer readable storage medium.

[0147] As used herein, the terms "computer-readable data carrier" and "computer-readable storage medium" may specifically refer to non-transitory data storage means such as hardware storage media on which computer-executable instructions are stored. A computer-readable data carrier or storage medium may specifically be or include a storage medium such as a random access memory (RAM) and / or a read-only memory (ROM).

[0148] Thus, in particular, one, two or more or even all of the above method steps, such as method step d), may be carried out by using a computer or a computer network, preferably by using a computer program.

[0149] Further disclosed and proposed herein is a computer program product having program code means for carrying out the method according to the invention in one or more of the embodiments contained herein when the program is executed on a computer or a computer network. In particular, the program code means may be stored on a computer readable data carrier and / or a computer readable storage medium.

[0150] Further disclosed and proposed in this specification is a data carrier having stored thereon a data structure which, after being loaded into a computer or computer network, for example into a working memory or main memory of the computer or computer network, is capable of performing the methods according to one or more of the embodiments disclosed in this specification.

[0151] Further disclosed and proposed herein is a computer program product having program code means stored on a machine-readable carrier for performing the method according to one or more of the embodiments disclosed herein when the program is executed on a computer or a computer network. As used herein, a computer program product refers to a program as a tradeable product. The product may generally be present in any format, such as in paper format or on a computer-readable data carrier and / or a computer-readable storage medium. In particular, the computer program product may be distributed over a data network.

[0152] Finally, disclosed and suggested herein is a modulated data signal containing instructions readable by a computer system or computer network for carrying out a method according to one or more of the embodiments disclosed herein.

[0153] With reference to computer-implemented aspects of the present invention, one or more of the method steps of the method according to one or more of the embodiments disclosed herein, or even all of the method steps, may be performed by using a computer or a computer network. Thus, in general, any of the method steps involving providing and / or manipulating data may be performed by using a computer or a computer network. In general, these method steps may include any of the method steps that typically exclude method steps that require manual labor, such as certain aspects of providing a sample and / or performing the actual measurement.

[0154] Specifically, further disclosed herein is a method for producing a medicament for use in a method for treating a medicament comprising: - a computer or a computer network including at least one processor, the processor being adapted to execute a method according to one of the embodiments described herein; - a computer-loadable data structure adapted to carry out a method according to one of the embodiments described herein while said data structure is executed on a computer; - a computer program adapted to carry out a method according to one of the embodiments described herein while said program is running on a computer, a computer program comprising program means for carrying out a method according to one of the embodiments described herein while said computer program is being run on a computer or a computer network, a computer program comprising program means according to the previous embodiment, the program means being stored on a computer readable storage medium; and a storage medium, on which a data structure is stored, the data structure being adapted to execute a method according to one of the embodiments described herein after being loaded into a main and / or working storage of a computer or a computer network, a computer program product having program code means, which may be stored or have been stored on a storage medium for performing a method according to one of the embodiments described herein when the program code means is executed on a computer or a computer network; It is.

[0155] In a further aspect, a mobile device is disclosed comprising at least one detector according to the invention. The mobile device is one or more of a mobile communication device, a tablet computer, a handheld computer. With regard to the definitions and embodiments of the mobile device, reference is made to the definitions and embodiments described with regard to the detector and the method.

[0156] In a further aspect of the invention, a method of using a detector according to the invention, such as according to one or more of the embodiments described above or as shown in more detail below, for a purpose of use selected from the group consisting of: position measurement in traffic technology, entertainment applications, security applications, surveillance applications, safety applications, human-machine interface applications, logistics applications, tracking applications, outdoor applications, mobile applications, communication applications, photography applications, machine vision applications, robotics applications, quality control applications, manufacturing applications, pedestrian monitoring applications, body monitoring applications, home care, smart living, automotive applications is proposed.

[0157] Regarding further methods of using the detectors and devices of the present invention, reference is made to WO2018 / 091649A1, WO2018 / 091638A1, and WO2018 / 091640A1, the contents of which are incorporated by reference.

[0158] As used herein, the terms "have", "comprise" or "include" or any grammatical variants thereof are used non-exclusively. These terms can therefore refer both to the situation in which no further features are present in the entity described in this context, in addition to the features introduced by these terms, and to the situation in which one or more further features are present. As an example, the expressions "A has B", "A comprises B", "A includes B" can refer both to the situation in which, apart from B, no other elements are present in A (i.e., A is solely and exclusively composed of B), and to the situation in which, apart from B, one or more further elements are present in the entity A, such as element C, elements C and D, or further elements.

[0159] Furthermore, it should be noted that the terms "at least one," "one or more," or similar language indicating that a feature or element may be present one or more times, are typically used only once when introducing each feature or element. In most cases, the language "at least one" or "one or more" will not be repeated when referring to each feature or element, regardless of the fact that each feature or element may be present one or more times.

[0160] Furthermore, as used herein, the terms "preferably", "more preferably", "particularly", "more particularly", "specifically", "more specifically", or similar terms are used with any feature without limiting alternative possibilities. Thus, features introduced by these terms are optional features and are not intended to limit the scope of the claims in any way. The present invention may be practiced by using alternative features, as will be recognized by those skilled in the art. Similarly, features introduced by "in an embodiment of the invention" or similar expressions are intended to be optional features, without limitations on alternative embodiments of the invention, without limitations on the scope of the invention, and without limitations on the possibility of combining features introduced in such a way with other optional or non-optional features of the invention.

[0161] In summary, without excluding further possible embodiments, the following embodiments may be envisaged:

[0162] EMBODIMENT 1 1. A detector for determining at least one material property of at least one object, comprising: - at least one projector configured to illuminate an object with at least one illumination pattern including a plurality of illumination features; - at least one first camera having at least one first sensor element, the first sensor element having a matrix of first optical sensors, each first optical sensor having a photosensitive area, each first optical sensor designed to generate at least one sensor signal in response to illumination of the respective photosensitive area by a reflected light beam propagating from the object to the first camera, the first camera configured to capture at least one first reflected image comprising a plurality of first reflected features generated by the object in response to illumination by the illumination features, the first camera being arranged such that the first reflected image is captured in a first line of sight direction relative to the object; - at least one second camera having at least one second sensor element, the second sensor element having a matrix of second optical sensors, each second optical sensor having a photosensitive area, each second optical sensor designed to generate at least one sensor signal in response to illumination of the respective photosensitive area by a reflected light beam propagating from the object to the second camera, the second camera being configured to capture at least one second reflected image comprising a plurality of second reflection features generated by the object in response to illumination by the illumination features, the second camera being arranged such that the second reflected image is captured in a second line of sight direction relative to the object, the second camera being different from the first line of sight direction and the second line of sight direction; - at least one evaluation device configured to evaluate the first and second reflected images, the evaluation comprising matching the first and second reflected features and determining combined material properties of the matched pair of first and second reflected features by analysis of their beam profiles; A detector comprising:

[0163] EMBODIMENT 2 2. A detector as described in embodiment 1, wherein a baseline between the first camera and the projector is shorter than a baseline between the second camera and the projector.

[0164] EMBODIMENT 3 A detector as described in embodiment 1 or 2, wherein the evaluation device is configured to determine a bidirectional reflectance distribution function of a matched pair of first and second reflectance features, and the evaluation device is configured to determine a combined material property by evaluating the bidirectional reflectance distribution function.

[0165] EMBODIMENT 4 4. A detector as described in embodiment 3, wherein evaluating the bidirectional reflectance distribution function comprises comparing the bidirectional reflectance distribution function to at least one predefined bidirectional reflectance distribution function.

[0166] EMBODIMENT 5 A detector according to any one of embodiments 1 to 4, wherein the evaluation device is configured to determine a first material property by analyzing a beam profile of a first reflection feature and to determine a second material property by analyzing a beam profile of a second reflection feature, and the evaluation device is configured to combine the first material property and the second material property to determine a combined material property.

[0167] EMBODIMENT 6 6. A detector according to any one of embodiments 1 to 5, wherein the illumination pattern comprises at least 4000 illumination features.

[0168] EMBODIMENT 7 The detector according to any one of the first to sixth embodiments, wherein the projector comprises at least one emitter and / or at least one array of emitters, each of the emitters being and / or comprising at least one element selected from the group consisting of at least one laser source, such as at least one semiconductor laser, at least one double heterostructure laser, at least one external cavity laser, at least one separate confinement heterostructure laser, at least one quantum cascade laser, at least one distributed Bragg reflector laser, at least one polariton laser, at least one hybrid silicon laser, at least one extended cavity diode laser, at least one quantum dot laser, at least one volume Bragg grating laser, at least one indium arsenide laser, at least one gallium arsenide laser, at least one transistor laser, at least one diode pumped laser, at least one distributed feedback laser, at least one quantum well laser, at least one interband cascade laser, at least one semiconductor ring laser, at least one vertical cavity surface emitting laser, at least one non-laser light source, such as at least one LED or at least one light bulb.

[0169] EMBODIMENT 8 A detector according to any one of embodiments 1 to 7, wherein the first camera includes at least one CCD sensor or at least one CMOS sensor, and the second camera includes at least one CCD sensor or at least one CMOS sensor.

[0170] EMBODIMENT 9 A detector according to any one of the preceding embodiments, wherein the evaluation device is configured to determine an ordinate for each of the first reflection features by analysis of the respective beam profiles.

[0171] EMBODIMENT 10 A detector as described in embodiment 9, wherein the analysis of the beam profile includes determining at least one first area and at least one second area of ​​the beam profile, and the evaluation device is configured to derive the combined signal Q by one or more of dividing the first area and the second area, dividing a multiple of the first area and the second area, or dividing a linear combination of the first area and the second area, and the evaluation device is configured to use at least one predetermined relationship between the combined signal Q and the ordinate to determine the ordinate.

[0172] EMBODIMENT 11 A detector as described in embodiment 9 or 10, wherein the evaluation device is configured to match the first reflection feature and the second reflection feature by using the determined ordinate coordinate, the evaluation device is configured to determine a longitudinal area for each of the first reflection features, the longitudinal area being given by the ordinate coordinate of the reflection feature and an error interval ±ε, and the evaluation device is configured to determine at least one displacement area in the second reflection image corresponding to the longitudinal area.

[0173] EMBODIMENT 12 A detector as described in embodiment 11, wherein the evaluation device is configured to match one of the first reflection features to one of the second reflection features respectively within the displacement region.

[0174] EMBODIMENT 13 A detector as described in any one of embodiments 1 to 12, wherein the evaluation device is configured to determine a displacement of matched first and second reflection features, the displacement being a difference between an image position of the first reflection feature in the first reflection image and an image position of the second reflection feature in the second reflection image, and the evaluation device is configured to determine a refined ordinate by triangulation using a predetermined relationship between the ordinate and the displacement.

[0175] EMBODIMENT 14 A detector as described in any one of embodiments 1 to 13, wherein the detector is configured to automatically calibrate each of the first camera and the second camera to the projector and / or to automatically calibrate the first camera and the second camera to each other.

[0176] EMBODIMENT 15 A method for determining material properties of at least one object using at least one detector for identifying at least one material property according to any one of embodiments 1 to 14, the method comprising the steps of: a) illuminating an object with at least one illumination pattern including a plurality of illumination features by using a projector; b) capturing at least one first reflected image comprising a plurality of first reflective features produced by the object in response to illumination by the illumination features by using a first camera, the first camera being arranged such that the first reflected image is captured in a first line of sight direction relative to the object; c) capturing at least one second reflected image comprising a plurality of second reflected features produced by the object in response to illumination by the illumination features by using a second camera, the second camera being arranged such that the second reflected image is captured at a second line of sight direction relative to the object, the second line of sight direction being different from the first line of sight direction and the second line of sight direction; d) evaluating the first and second reflected images by using an evaluation device, the evaluation comprising matching the first and second reflected features and determining combined material properties of the matched pair of first and second reflected features by analysis of their beam profiles; The method includes:

[0177] EMBODIMENT 16 A computer program comprising instructions that, when executed by a detector according to any one of embodiments 1 to 14 referring to the detector, cause the detector to perform the method according to embodiment 15 referring to the method.

[0178] EMBODIMENT 17 A computer-readable storage medium comprising instructions that, when executed by a detector according to any one of embodiments 1 to 14 referring to the detector, cause the detector to perform the method according to embodiment 15 referring to the method.

[0179] EMBODIMENT 18 A mobile device comprising at least one detector according to any one of embodiments 1 to 14 referring to a detector, the mobile device being one or more of a mobile communication device, a tablet computer, a handheld computer.

[0180] EMBODIMENT 19 A method of using the detector according to any one of embodiments 1 to 14 referring to the detector for a purpose of use selected from the group consisting of position measurement in traffic technology, entertainment applications, security applications, surveillance applications, safety applications, human-machine interface applications, logistics applications, tracking applications, outdoor applications, mobile applications, communication applications, photography applications, machine vision applications, robotics applications, quality control applications, manufacturing applications, pedestrian monitoring applications, human body monitoring applications, home care, smart living, automotive applications.

[0181] Further optional features and embodiments are disclosed in more detail in the following description of the embodiments, preferably together with the dependent claims. Here, each optional feature can be realized in separate form and in any possible combination, as understood by a person skilled in the art. The scope of the present invention is not limited by the preferred embodiments. The embodiments are illustrated diagrammatically in the drawings. The same reference symbols in these drawings refer to the same elements or functionally equivalent elements. [Brief description of the drawings]

[0182] [Figure 1] 1 shows an embodiment of a detector and a mobile device according to the present invention; [Diagram 2] FIG. 1 illustrates an embodiment of a method for determining at least one material property according to the present invention. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0183] 1 shows very diagrammatically an embodiment of a detector 110 and a mobile device 114 for determining at least one material property of at least one object 112 according to the present invention. The detector 110 may be either attached to or integrated into the mobile device 114, such as a mobile phone or a smartphone. The detector 110 may be integrated into the mobile device 114, for example within a housing of the mobile device.

[0184] For example, the object 112 may be at least one object selected from the group consisting of a scene, a human being such as a person, wood, carpet, foam, an animal such as a cow, a plant, a piece of tissue, metal, a toy, a metal object, a food such as a beverage, fruit, meat, or fish, a dish, a makeup product, an applied makeup product, a fabric, a fur, hair, a maintenance product, a cream, an oil, a powder, a carpet, juice, a suspension, a paint, a plant, a body, a body part, an organic material, an inorganic material, a reflective material, a screen, a display, a wall, a piece of paper such as a photograph, etc.

[0185] For example, the material property may be a property selected from the group consisting of roughness, penetration depth of light into the material, a property characterizing the material as biological or non-biological, reflectance, specular reflectance, diffuse reflectance, surface properties, a measure of translucency, scattering, especially backscattering behavior, etc. The at least one material property may be a property selected from the group consisting of scattering coefficient, translucency, transparency, deviation from Lambertian surface reflectance, speckle, etc.

[0186] Determining the at least one material property may include one or more of identifying the material property, characterizing the material property, and assigning the material property to the object 112. The detector 110 may include at least one database including a list and / or table, such as a look-up list or look-up table, of predefined and / or predetermined material properties. The list and / or table of material properties may be determined and / or generated by performing at least one test measurement using the detector, such as by performing material testing using a sample having known material properties. The list and / or table of material properties may be determined and / or generated at the manufacturer site and / or by a user of the detector. The material property may be further assigned to a material classifier, such as one or more of a material name, a material group, such as biological or non-biological material, translucent or non-translucent material, metal or non-metal, skin or non-skin, fur or non-fur, carpet or non-carpet, reflective or non-reflective, specular or non-specular, foam or non-foam, hair or non-hair, roughness group, and the like. The detector 110 may include at least one database that includes lists and / or tables containing material properties and associated material names and / or material groups.

[0187] The detector 110 is at least one projector 116 configured to illuminate the object 112 with at least one illumination pattern 118 including a plurality of illumination features 120; at least one first camera 122 having at least one first sensor element, the first sensor element having a matrix of first optical sensors, each first optical sensor having a photosensitive area, each first optical sensor designed to generate at least one sensor signal in response to illumination of the respective photosensitive area by a reflected light beam propagating from the object 112 to the first camera 122, the first camera being configured to capture at least one first reflected image comprising a plurality of first reflection features generated by the object 112 in response to illumination by the illumination features, the first camera 122 being arranged such that the first reflected image is captured in a first line of sight direction relative to the object 112; at least one second camera 124 having at least one second sensor element, the second sensor element having a matrix of second optical sensors, each second optical sensor having a photosensitive area, each second optical sensor designed to generate at least one sensor signal in response to illumination of the respective photosensitive area by a reflected light beam propagating from the object 112 to the second camera 124, the second camera being configured to capture at least one second reflected image comprising a plurality of second reflection features generated by the object 112 in response to illumination by the illumination features, the second camera 124 being arranged such that the second reflected image is captured in a second line of sight direction relative to the object 112, different from the first line of sight direction and the second line of sight direction; at least one evaluation device 126 configured to evaluate the first and second reflected images, the evaluation comprising matching the first and second reflected features and determining combined material properties of the matched pair of first and second reflected features by analysis of their beam profiles; Equipped with.

[0188] Projector 116 may be an optical device configured to project at least one illumination pattern 118 onto object 112, specifically onto a surface of object 112. Illumination feature 120 may be at least one at least partially extended feature of illumination pattern 118. Illumination pattern 118 includes a plurality of illumination features 120. For example, illumination pattern 118 includes at least 4000 illumination features 120 or even more illumination features.

[0189] The illumination pattern 118 may be a regular, in particular a periodic pattern. However, other non-regular patterns may also be possible. The illumination pattern 118 may include at least one pattern selected from the group consisting of at least one quasi-random pattern, at least one Sobol pattern, at least one quasi-periodic pattern, at least one point pattern, in particular a pseudo-random point pattern, at least one line pattern, at least one stripe pattern, at least one checkerboard pattern, at least one triangular pattern, at least one rectangular pattern, at least one hexagonal pattern, or a pattern including further convex tilings. The illumination pattern 118 may exhibit at least one illumination feature 120 selected from the group consisting of at least one point, at least one line, at least two lines such as parallel or crossed lines, at least one point and one line, at least one sequence of periodic features, at least one pattern of arbitrarily shaped features. For example, the illumination pattern 118 includes at least one pattern including at least one pre-known feature. For example, the illumination pattern 118 includes at least one line pattern including at least one line. For example, the illumination pattern 118 includes at least one line pattern including at least two lines, such as parallel lines or intersecting lines. For example, the projector 116 may be configured to generate and / or project a point cloud or non-point-like features. For example, the projector 116 may be configured to generate a point cloud or non-point-like features such that the illumination pattern 118 may include multiple point or non-point-like features.

[0190] The projector 116 may include at least one emitter and / or at least one array of emitters, each of which may be configured to emit at least one beam of light. Each of the emitters may be and / or include at least one element selected from the group consisting of at least one laser source, such as at least one semiconductor laser, at least one double heterostructure laser, at least one external cavity laser, at least one separate confinement heterostructure laser, at least one quantum cascade laser, at least one distributed Bragg reflector laser, at least one polariton laser, at least one hybrid silicon laser, at least one extended cavity diode laser, at least one quantum dot laser, at least one volume Bragg grating laser, at least one indium arsenide laser, at least one gallium arsenide laser, at least one transistor laser, at least one diode pumped laser, at least one distributed feedback laser, at least one quantum well laser, at least one interband cascade laser, at least one semiconductor ring laser, at least one vertical cavity surface emitting laser (VCSEL), at least one non-laser light source, such as at least one LED or at least one light bulb. For example, the emitter may be an array of VCSELs. As used herein, the term "vertical cavity surface emitting laser" is a broad term and is intended to have the usual and customary meaning to those skilled in the art, and is not limited to any special or special meaning. The term may specifically refer to, but is not limited to, a semiconductor laser diode configured to emit a laser beam perpendicular to its top surface. Examples of VCSELs can be found, for example, at en.wikipedia.org / wiki / Vertical-cavity_surface-emitting_laser. VCSELs are generally known to those skilled in the art, such as from WO2017 / 222618A. Each of the VCSELs is configured to generate at least one light beam. The VCSELs may be arranged on a common substrate or on different substrates.An array may contain up to 2500 VCSELs. For example, an array may contain 38 x 25 VCSELs, such as a high power array at 3.5 W. For example, an array may contain 10 x 27 VCSELs at 2.5 W. For example, an array may contain 96 VCSELs at 0.9 W. For example, a 2500 element array may be up to 2 millimeters by 2 millimeters in size.

[0191] The light beam emitted by each emitter may have a wavelength of 300 to 1100 nanometers, preferably 500 to 1100 nanometers. For example, the light beam may have a wavelength of 940 nanometers. Light in the infrared spectral range may be used, for example in the range of 780 nanometers to 3.0 μm. In particular, light in the part of the near infrared range, in particular in the range of 700 nanometers to 1100 nanometers, for which silicon photodiodes are applicable, may be used. The emitters may be configured to generate at least one illumination pattern in the infrared range, in particular in the near infrared range. Using light in the near infrared range may enable light that is not detectable by the human eye or is only weakly detectable, but can be detected by a silicon sensor, in particular a standard silicon sensor. For example, the emitters may be an array of VCSELs. The VCSELs may be configured to emit light beams in the wavelength range of 800 to 1000 nanometers. For example, the VCSEL may be configured to emit a light beam at 808 nanometers, 850 nanometers, 940 nanometers, or 980 nanometers. Preferably, the VCSEL emits light at 940 nanometers because terrestrial solar radiation has a local minimum irradiance at this wavelength, as described, for example, in CIE 085-1989 "Solar spectral Irradiance."

[0192] The projector 116 may include at least one transfer device (not shown), which is configured to generate the illumination feature 120 from the light beam impinging on the transfer device. The transfer device may include at least one imaging optical device. The transfer device may specifically include one or more of at least one lens, for example at least one lens selected from the group consisting of at least one adjustable focus lens, at least one aspheric lens, at least one spherical lens, at least one Fresnel lens, at least one diffractive optical element, at least one concave mirror, at least one beam deflecting element, preferably at least one mirror, at least one beam splitting element, preferably at least one of a beam splitting cube or a beam splitting mirror, at least one multi-lens system, at least one holographic optical element, at least one meta-optical element. In particular, the transfer device includes at least one refractive optical lens stack. Thus, the transfer device may include a multi-lens system having refractive properties. The projector 116 may include at least one diffractive optical element (DOE) configured to generate the illumination pattern 118. The DOE can be configured to generate multiple light beams from a single incident light beam.

[0193] The object 112 may include at least one surface onto which the illumination pattern 118 is projected. The surface may be adapted to at least partially reflect the illumination pattern 118 towards the detector. For example, without wishing to be bound by theory, human skin may have a reflection profile, also called a backscattering profile, that includes a portion produced by retroreflection of the surface, called the surface reflection, and a portion produced by very diffuse reflection from the light penetrating the skin, called the diffuse portion of the retroreflection. For the reflection profile of human skin, see "Lasertechnik in der Medizin: Grundlagen, Systeme, Anwendungen", "Wirkung von Laserstrahlung auf Gewebe", 1991, pp. 171-266, Juergen Eichler, Theo Seiler, Springer Verlag, ISBN 0939-0979. The surface reflection of the skin may increase as the wavelength increases towards the near infrared. Furthermore, the penetration depth may increase as the wavelength increases from visible light towards the near infrared. The diffuse portion of the retroreflection can increase with the penetration depth of the light. These material properties can be used to distinguish skin from other materials, specifically by analyzing the retroscattering profile.

[0194] The first camera 122 may include at least one CCD sensor or at least one CMOS sensor. The second camera 124 may include at least one CCD sensor or at least one CMOS sensor.

[0195] The first camera 122 and the second camera 124 may be designed to be the same or different. For example, the first camera 122 and the second camera 124 may have the same resolution or different resolutions.

[0196] The first camera 122 is configured to capture at least one first reflected image including a first plurality of reflected features generated by the object 112 in response to illumination by the illumination feature 120. The second camera 124 is configured to capture at least one second reflected image including a second plurality of reflected features generated by the object 112 in response to illumination by the illumination feature 120. The reflected images may be images determined by the respective cameras including a plurality of reflected features. The reflected features may be features of an image plane generated by the object 112 in response to illumination by the at least one illumination feature 118. The reflected image may include at least one reflected pattern including the reflected features. Capturing the at least one reflected image may include one or more of capturing, recording, and generating the reflected image.

[0197] The first camera 122 is arranged such that a first reflected image is captured in a first line of sight direction relative to the object 112. The second camera 124 is arranged such that a second reflected image is captured in a second line of sight direction relative to the object 112. The first line of sight direction and the second line of sight direction are different.

[0198] A viewing direction relative to an object, also referred to as a line of sight, may be defined by the three-dimensional position and / or orientation of each camera 122, 124 in space. The viewing direction may be defined by the 3D coordinates of the cameras 122, 124, such as x, y, and z. The viewing direction may be defined by a rotation of the cameras 122, 124, for example about one or more of the x-axis, y-axis, and z-axis. The rotation may be given by a spatial angle.

[0199] The first camera 122 and the second camera 124 may be positioned at different spatial positions. The relative distance between the first camera 122 and the second camera 124 may be fixed. For example, the first camera 122 and the second camera 124 may be arranged as a stereo camera in the housing of the detector. As shown in FIG. 1, the configuration of the detector 110 may be such that the first camera 122 may be positioned on one side of the projector 116 and the second camera 124 may be positioned on the opposite side of the projector 116. For example, the baseline between the first camera 122 and the projector 116 may be shorter than the baseline between the second camera 124 and the projector 116. The baseline between the second camera 124 and the projector 116 may be, for example, four times, five times, or more than the baseline between the first camera 122 and the projector 116. For example, the baseline between the first camera 122 and the projector 116 may be 4 cm or less.

[0200] As shown in FIG. 1, the first camera 122 and the second camera 124 may have different orientations towards the object 112. For example, the first sensor element may be oriented essentially perpendicular to the optical axis of the detector 110. The line of sight direction of the first camera 122 may be essentially parallel to the optical axis of the detector 110. The second sensor element may be oriented to deviate from an essentially perpendicular alignment to the optical axis of the detector 110. The line of sight direction of the second camera 124 may not be essentially parallel to the optical axis of the detector 110 and may form an angle with the optical axis.

[0201] The evaluation device 126 is configured to evaluate the first reflected image and the second reflected image.

[0202] The evaluation device 126 may be configured to identify and / or select a first reflection feature in the first reflection image and a second reflection feature in the second reflection image. The evaluation device 126 may be configured to perform at least one image analysis and / or image processing to identify and / or select the first reflection feature and the second reflection feature. The image analysis and / or image processing may use at least one feature detection algorithm. The image analysis and / or image processing may include filtering, selecting at least one region of interest, forming a difference image between an image generated by the sensor signal and at least one offset, inverting the sensor signal by inverting the image generated by the sensor signal, forming a difference image between images generated by the sensor signal at different times, background correction, decomposition into color channels, decomposition into hue, saturation, luminance channel, frequency decomposition, singular value decomposition, applying a blob detector, applying a corner detector, applying a determinant of a Hessian filter, applying a principal curvature based region detector, applying a maximum stable extremum region detector, applying a generalized Hough transform, applying a ridge detector, applying an affine invariant feature detector, applying an affine adaptive interest point operator, applying a Harris affine region detector, applying a Hessian affine region detector, applying a scale invariant feature transform, applying a scale space extremum The method may include one or more of: applying a detector, applying a local feature detector, applying a fast robust feature algorithm, applying a histogram of gradient position and orientation algorithm, applying a histogram of oriented gradient descriptors, applying a Deriche edge detector, applying a differential edge detector, applying a spatio-temporal interest point detector, applying a Moravec corner detector, applying a Canny edge detector, applying a Laplace operator of Gaussian filters, applying a difference of Gaussian filters, applying a Sobel operator, applying a Laplace operator, applying a Scharr operator, applying a Prewitt operator, applying a Roberts operator, applying a Kirsch operator, applying a high pass filter, applying a low pass filter, applying a Fourier transform, applying a Radon transform, applying a Hough transform, applying a wavelet transform, thresholding, and generating a binary image. In particular, evaluating the reflectance image includes selecting a region of interest in the reflectance image.The region of interest may be determined manually by a user or may be determined automatically, such as by recognizing an object in an image produced by the sensor elements. For example, in the case of a spot-like reflective feature, the region of interest may be selected as the area around the spot profile.

[0203] The evaluation device 126 may be configured to perform at least one image correction, which may include at least one background subtraction, and may be adapted to remove background light effects from the respective beam profile, for example by imaging without further illumination.

[0204] The evaluation includes matching the first and second reflection features. The matching may include determining and / or evaluating and / or identifying corresponding first and second reflection features. The matching may include solving a so-called correspondence problem. The evaluation device 126 may be configured to unambiguously match the first and second reflection features.

[0205] Each of the first and second reflective features includes at least one beam profile. The beam profile may be a transverse intensity profile of the light beam. The beam profile may be a cross-section of the light beam. The beam profile may be selected from the group consisting of a trapezoidal beam profile, a triangular beam profile, a conical beam profile, and a linear combination of a Gaussian beam profile. However, other embodiments are possible. The beam profile may also be referred to as a reflective profile.

[0206] The evaluation device 126 may be configured to determine an ordinate for each of the first reflection features by analysis of the respective beam profiles. The analysis of the beam profiles may include determining at least one first area and at least one second area of ​​the beam profiles. The evaluation device 126 may be configured to derive the combined signal Q by one or more of dividing the first area and the second area, dividing a multiple of the first area and the second area, dividing a linear combination of the first area and the second area. The evaluation device may be configured to use at least one predetermined relationship between the combined signal Q and the ordinate to determine the ordinate.

[0207] The evaluation device 126 determines, by analysis of the beam profile, for each of the first reflective features, z DPR The analysis of the beam profile may include at least one mathematical operation and / or at least one comparison and / or at least one symmetrization and / or at least one filtering and / or at least one normalization. For example, the analysis of the beam profile may include at least one of the following: a histogram analysis step, a calculation of a difference measure, application of a neural network, application of a machine learning algorithm. The evaluation device 126 may be configured to symmetrize and / or normalize and / or filter the beam profile, in particular to remove noise or asymmetries from recordings at larger angles, recordings of edges, etc. The evaluation device 126 may filter the beam profile by removing high spatial frequencies, for example by spatial frequency analysis and / or median filtering. The aggregation may be performed by center of intensity of the light spot and averaging of all intensities at the same distance to the center. The evaluation device 126 may be configured to normalize the beam profile to a maximum intensity, in particular to take into account intensity differences due to the recorded distance. The evaluation device 126 may be configured to remove the effects of background light from the beam profile, for example by imaging without illumination.

[0208] The evaluation device 126 may be configured to determine an ordinate for each of the first reflective features by using a depth ratio from photons technique, also called beam profile analysis. Regarding the depth ratio from photons (DPR) technique, see WO2018 / 091649A1, WO2018 / 091638A1, and WO2018 / 091640A1, and C. Lennartz, F. Schick, S. Metz, "Whitepaper-Beam Profile Analysis for 3D imaging and material detection", April 28, 2021, Ludwigshafen, Germany, the entire contents of which are incorporated by reference.

[0209] The ordinate of the first reflection feature may be a distance between the first camera 122 and the object 112. The evaluation device 126 may be configured to use at least one predetermined relationship between the combined signal and the ordinate to determine the ordinate. The predetermined relationship may be one or more of an empirical relationship, a semi-empirical relationship, and an analytically derived relationship. The evaluation device 126 may include at least one data storage device for storing the predetermined relationship, such as a look-up list or a look-up table. The evaluation device 126 may be configured to execute a depth ratio algorithm from at least one photon that calculates distances for all first reflection features of zero order and higher order.

[0210] The evaluation device 126 may be configured to match the first and second reflection characteristics by using the determined ordinate.

[0211] The evaluation device 126 may be configured to determine a longitudinal area for each of the first reflection features, the longitudinal area being given by the ordinate of the reflection feature and an error interval ±ε.

[0212] The evaluation device 126 may be adapted to identify at least one second reflection feature in the second reflection image having essentially the same ordinate as the selected first reflection feature. The second reflection feature corresponding to the first reflection feature may be determined using epipolar geometry. For a description of epipolar geometry, see, for example, X. Jiang, H. Bunke, Chapter 2: "Dreidimensionales Computersehen", Springer, Berlin Heidelberg, 1997. In epipolar geometry, it may be assumed that the first reflection image and the second reflection image are images of an object determined at different spatial positions and / or spatial orientations with a fixed distance. The evaluation device 126 may be adapted to determine an epipolar line in the second reflection image. The baseline of the first reflection image (of the first camera) and the second reflection image (of the second camera) may be known. For example, the relative positions of the first and second reflected images may be stored in at least one storage unit of the evaluation device 126. The evaluation device 126 may be adapted to determine a straight line extending from the selected first reflected feature. The straight line may include possible object features corresponding to the selected first reflected feature. The straight line and the baseline span the epipolar plane. Since the second reflected image is determined at a different relative position than the first reflected image, the corresponding possible object features may be imaged on a straight line, called an epipolar line, in the second reflected image. Thus, the features of the second reflected image corresponding to the selected first reflected feature lie on the epipolar line.

[0213] The evaluation device 126 may be configured to determine at least one displacement region in the second reflected image corresponding to the longitudinal region. In particular, the displacement region may be a region in the second reflected image where a second reflection feature corresponding to the selected first reflection feature is expected to be located in the second reflected image. Depending on the distance to the object 112, the image position of the second reflection feature corresponding to the first reflection feature may be displaced in the second reflected image compared to the image position of the first reflection feature in the first reflected image. The displacement region may include only one second reflection feature. The displacement region may also include more than one second reflection feature. The displacement region may include an epipolar line or a part of an epipolar line. The displacement region may include more than one epipolar line or more parts of more than one epipolar line. The displacement region may extend along the epipolar line, or be perpendicular to the epipolar line, or both. The evaluation device 126 may be adapted to determine a second reflection feature along the epipolar line corresponding to the distance feature and to determine the extent of a displacement area along the epipolar line or orthogonal to the epipolar line corresponding to the error interval ±ε. The measurement uncertainty of the distance estimation may give rise to a non-circular displacement area since the measurement uncertainty may be different for different directions. In particular, the measurement uncertainty along one or more epipolar lines may be larger than the measurement uncertainty in a direction orthogonal to the one or more epipolar lines. The displacement area may include an extension in a direction orthogonal to the one or more epipolar lines.

[0214] The evaluation device 126 may be configured to match one of the first reflection features to one of the second reflection features in the displacement regions, respectively. The evaluation device 126 may be configured to match the first reflection feature to the second reflection feature in the respective displacement region by using at least one evaluation algorithm taking into account the determined ordinate. The evaluation algorithm may be a linear scaling algorithm.

[0215] The evaluation device 126 may be configured to determine a displacement of the matched first and second reflection features. The displacement may be a difference between an image position of the first reflection feature in the first reflection image and an image position of the second reflection feature in the second reflection image. The image position may be a pixel coordinate in the respective reflection image. The evaluation device 126 may be configured to determine the refined ordinate by triangulation using a predetermined relationship between the ordinate and the displacement. High accuracy is obtained by using triangulation with a second camera having a higher baseline. The predetermined relationship may be one or more of an empirical relationship, a semi-empirical relationship, and an analytically derived relationship. The evaluation device 126 may include at least one data storage device for storing the predetermined relationship, such as a look-up list or a look-up table. The evaluation device 126 may be adapted to determine the predetermined relationship by using a triangulation method. If the position of the selected first reflection feature in the first reflection image, the position of the matched second reflection feature, and / or the relative displacement of the matched first and second reflection features are known, the refined ordinates may be determined by triangulation. Thus, the evaluation device 126 may be adapted to select first reflection features, for example consecutively and / or row by row, and to determine, for each potential position of the second reflection feature, a corresponding distance value using triangulation. The displacements and corresponding distance values ​​may be stored in at least one storage device of the evaluation device 126.

[0216] The evaluation device 126 is configured to determine a combined material property of the matched pair of first and second reflection features by analyzing the beam profile. The combined material property may be a material property determined by using and / or taking into account information from the first and second reflection features. Determining the combined material property may include determining a material property for each of the first and second reflection features and combining the determined material property with the combined material property. This may reduce the measurement uncertainty. The projector 116 may project an illumination feature 120 onto the object 112. Both cameras 122, 124 may image the object 112 with the projected illumination feature 118. The beam profile of the first and second reflection features may be evaluated and may allow to improve the quality of the determination of the material property. The beam profile analysis may allow to extract additional material information of the object 112. This configuration using two cameras 122, 124 may improve material detection, especially with respect to robustness. Two different views of the illuminated features on the object 112 can provide more information of the material scattering properties. Using two views can double the sample size, which can allow for forced feature-based material detection. A more robust material determination can be possible due to material roughness due to speckle noise, backscattered light intensity, sub-scattering properties of the material, and noise reduction due to the larger sample size. Using two views can be particularly advantageous for highly reflective materials such as plastic foils.

[0217] The determination of the material properties of each reflection feature may be performed using beam profile analysis. In particular, the beam profile analysis utilizes the reflection properties of the coherent light projected on the object surface to classify the material. The material classification may be performed as described in one of WO2020 / 187719, WO2021 / 170791A1, and / or WO2021 / 152070, the entire contents of which are incorporated by reference. In particular, the analysis of the beam profile of each reflection feature recorded by the camera may be performed by a feature-based method. Additionally or alternatively, machine learning methods may be used. The feature-based method may be used in combination with the machine learning method to allow parameterization of the skin classification model. Alternatively or in combination, a convolutional neuron network may be used to classify the skin by using the reflection image as input.

[0218] Additionally or alternatively, material properties may be determined using a bidirectional reflectance distribution function 128 of the matched first and second reflectance features. The evaluation device 126 may be configured to determine the bidirectional reflectance distribution function 128 of the matched first and second reflectance features. The evaluation device 126 may be configured to determine material properties by evaluating the bidirectional reflectance distribution function 128. The bidirectional reflectance distribution function (BRDF) 128 may be a luminance distribution as a function of viewing angle. The BRDF 128 may be a combined beam profile determined by using two different views on each illumination feature. The material properties may be modeled by the bidirectional reflectance distribution function 128. For example, the actual appearance of a spatially uniform material may be represented by the BRDF 128, as described in J. Filip et al., "BRDF Measurement of Highly-Specular Materials using a Goniometer," a preprint submitted to SCCG 2017 in April 2017. The BRDF 128 defines how light reflects off the surface of the material in terms of illumination direction ω. i = {θ, φ i} and line of sight ω v = {θ v , φv} a 4-dimensional vector-valued function f r (θ i , θ v , φ i , φ v ), where θ∈[0,π / 2] is the elevation angle and φ ∈ [0,2π] is the azimuth angle in a spherical coordinate system. Two views of an illumination feature on an object may allow sampling of the BRDF128.

[0219] The evaluation of the bidirectional reflectance distribution function 128 may include comparing the bidirectional reflectance distribution function 128 to at least one predefined bidirectional reflectance distribution function. The predefined bidirectional reflectance distribution function may be stored in a table or a look-up table, may be determined, for example, empirically, and may be stored in at least one data store of the evaluation device 126, for example, by software, in particular by an app downloaded from an app store or the like.

[0220] A reflectance feature may be identified as being produced by a material, e.g. biological tissue, if the bidirectional reflectance distribution function 128 and a predefined bidirectional reflectance distribution function are identical, at least within a tolerance. The evaluation of the bidirectional reflectance distribution function 128 may include comparing the bidirectional reflectance distribution function 128 with at least one predefined bidirectional reflectance distribution function. The comparison may include superimposing the bidirectional reflectance distribution function 128 and the predefined bidirectional reflectance distribution function. The comparison may include determining a deviation, e.g. a sum of squared point-to-point distances, between the bidirectional reflectance distribution function 128 and the predefined bidirectional reflectance distribution function. The evaluation device 126 may be adapted to compare the determined deviation with at least one threshold value, and if the determined deviation is less than and / or equal to the threshold value, the surface is indicated as a particular material, e.g. biological tissue. The threshold value may be stored in a table or a look-up table, may be determined, e.g. empirically, and may be stored, for example, in at least one data storage device of the evaluation device 126.

[0221] 2 shows a flow chart of an embodiment of a method for determining material properties of at least one object 112 using at least one detector 110 for identifying at least one material property according to the present invention. With regard to the detector 110, reference is made to FIG.

[0222] The method steps may be performed in a given order or in a different order. Further, there may be one or more additional method steps not listed. Further, one, more than one, or even all of the method steps may be performed repetitively.

[0223] The method comprises the following steps: a) (130) illuminating an object 112 with at least one illumination pattern 118 including a plurality of illumination features 120 by using a projector 116; b) (132) capturing at least one first reflected image comprising a plurality of first reflected features generated by the object 112 in response to illumination by the illumination feature 120 by using a first camera 122, the first camera 122 being arranged such that the first reflected image is captured in a first line of sight direction relative to the object 112; c) (134) capturing at least one second reflected image comprising a plurality of second reflected features produced by the object 112 in response to illumination by the illumination feature 120 by using a second camera 124, the second camera 124 being arranged such that the second reflected image is captured at a second line of sight direction relative to the object 112, the second line of sight direction being different from the first line of sight direction and the second line of sight direction; d) (136) evaluating the first and second reflected images by using the evaluation device 126, the evaluation including matching the first and second reflected features and determining combined material properties of the matched first and second reflected feature pairs by analysis of their beam profiles. [Explanation of symbols]

[0224] 110 Detector 112 Object 114 Mobile Devices 116 Projector 118 Irradiation Pattern 120 Irradiation Characteristics 122 First Camera 124 Second Camera 126 Evaluation Devices 128 Bidirectional reflectance distribution function 130 irradiation 132 Capturing at least one first reflected image 134 Capturing at least one second reflected image 136 Evaluation of the First Reflection Image and the Second Reflection Image

Claims

1. A detector (110) for determining at least one material property of at least one object (112), comprising: at least one projector (116) configured to illuminate said object (112) with at least one illumination pattern (118) including a plurality of illumination features (120); at least one first camera (122) having at least one first sensor element, the first sensor element having a matrix of first optical sensors, each of the first optical sensors having a photosensitive area, each first optical sensor designed to generate at least one sensor signal in response to illumination of the respective photosensitive area by a reflected light beam propagating from the object (112) to the first camera (122), the first camera (122) being configured to capture at least one first reflected image comprising a plurality of first reflected features generated by the object (112) in response to illumination by the illumination feature (120), the first camera (122) being arranged such that the first reflected image is captured in a first line of sight direction relative to the object (112); at least one second camera (124) having at least one second sensor element, the second sensor element having a matrix of second optical sensors, each of the second optical sensors having a photosensitive area, each second optical sensor designed to generate at least one sensor signal in response to illumination of the respective photosensitive area by a reflected light beam propagating from the object (112) to the second camera (124), the second camera (124) being configured to capture at least one second reflected image comprising a plurality of second reflected features generated by the object (112) in response to illumination by the illumination features (120), the second camera (124) being arranged such that the second reflected image is captured in a second line of sight direction relative to the object (120), the second camera being different from the first line of sight direction and the second line of sight direction; at least one evaluation device (126) configured to evaluate the first and second reflection images, said evaluation comprising matching the first and second reflection features and determining combined material properties of the matched pair of first and second reflection features by analysis of their beam profiles; The detector comprises:

2. The detector (110) of claim 1, wherein a baseline between the first camera (122) and the projector (116) is shorter than a baseline between the second camera (124) and the projector (116).

3. 3. The detector (110) of claim 1 or 2, wherein the evaluation device (126) is configured to determine a bidirectional reflectance distribution function (128) of the matched pair of first and second reflectance features, and the evaluation device (126) is configured to determine the combined material property by evaluating the bidirectional reflectance distribution function (128).

4. The detector of claim 3 , wherein the evaluation of the bidirectional reflectance distribution function comprises comparing the bidirectional reflectance distribution function to at least one predefined bidirectional reflectance distribution function.

5. 3. The detector (110) of claim 1 or 2, wherein the evaluation device (126) is configured to determine a first material property by analyzing the beam profile of the first reflection feature and to determine a second material property by analyzing the beam profile of the second reflection feature, and the evaluation device (126) is configured to combine the first material property and the second material property to determine the combined material property.

6. The detector (110) of claim 1 or 2, wherein the illumination pattern comprises at least 4000 illumination features.

7. The detector (110) of claim 1 or 2, wherein the first camera (122) includes at least one CCD sensor or at least one CMOS sensor, and the second camera (124) includes at least one CCD sensor or at least one CMOS sensor.

8. 3. The detector of claim 1, wherein the evaluation device is configured to determine an ordinate for each of the first reflection features by analysis of a respective beam profile, the analysis of the beam profile comprising determining at least one first area and at least one second area of ​​the beam profile, the evaluation device is configured to derive a combined signal Q by one or more of dividing the first area and the second area, dividing a multiple of the first area and the second area, or dividing a linear combination of the first area and the second area, and the evaluation device is configured to use at least one predetermined relationship between the combined signal Q and the ordinate to determine the ordinate.

9. 9. The detector of claim 8, wherein the evaluation device is configured to match the first reflection feature with the second reflection feature by using the determined ordinate coordinate, the evaluation device is configured to determine a longitudinal area for each of the first reflection features, the longitudinal area being given by the ordinate of the reflection feature and an error interval ±ε, the evaluation device is configured to determine at least one displacement area in the second reflection image corresponding to the longitudinal area, and the evaluation device is configured to match one of the first reflection features with one of the second reflection features within the displacement area, respectively.

10. 3. The detector (110) of claim 1 or 2, wherein the evaluation device (126) is configured to determine a displacement of matched first and second reflection features, the displacement being a difference between an image position of the first reflection feature in the first reflection image and an image position of the second reflection feature in the second reflection image, and the evaluation device (126) is configured to determine a refined ordinate by triangulation using a predetermined relationship between the ordinate and the displacement.

11. 3. A method for determining material properties of at least one object (112) using at least one detector (110) for identifying at least one material property according to claim 1 or 2, said method comprising the steps of: a) (130) illuminating the object (112) with at least one illumination pattern (118) including a plurality of illumination features (120) by using the projector (116); b) (132) capturing at least one first reflected image comprising a plurality of first reflected features produced by the object (112) in response to illumination by the illumination feature (120) by using the first camera (122), the first camera (122) being arranged such that the first reflected image is captured in a first line of sight direction relative to the object (112); c) (134) capturing at least one second reflected image comprising a plurality of second reflected features produced by the object (112) in response to illumination by the illumination features by using the second camera (124), the second camera (124) being arranged such that the second reflected image is captured at a second line of sight direction relative to the object (112), the second line of sight direction being different from the first line of sight direction and the second line of sight direction; d) (136) evaluating the first and second reflected images by using the evaluation device (126), the evaluation comprising matching the first and second reflected features and determining combined material properties of the matched first and second reflected feature pairs by analysis of their beam profiles; A method comprising:

12. A computer program comprising instructions, when said program is executed by a detector (110) according to claim 1 or 2 referring to a detector, that causes said detector to carry out the method according to claim 11.

13. A computer readable storage medium comprising instructions which, when executed by a detector (110) according to claim 1 or 2 referring to a detector, cause said detector to perform the method according to claim 11.

14. A non-transitory computer-readable medium comprising instructions that, when executed by one or more processors, cause the one or more processors to perform the method of claim 11.

15. A mobile device (114) comprising at least one detector (110) according to claim 1 or 2 referring to a detector, the mobile device being one or more of a mobile communication device, a tablet computer, a handheld computer.

16. A method of using the detector (110) according to claim 1 or 2, referring to the detector for a purpose of use selected from the group consisting of position measurement in traffic technology, entertainment applications, security applications, surveillance applications, safety applications, human-machine interface applications, logistics applications, tracking applications, outdoor applications, mobile applications, communication applications, photography applications, machine vision applications, robotics applications, quality control applications, manufacturing applications, pedestrian monitoring applications, human body monitoring applications, home care, smart living, automotive applications.

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