Measuring device, measuring system and measuring method

Abstract

The present invention provides a measuring device, a measuring system, a measuring program, and a measuring method for measuring the reflection characteristics of a desired object with a small number of samples and at a higher speed and with higher accuracy. According to one embodiment of the present invention, a measuring device for reflection characteristics is provided, characterized in that it has a control unit, and the control unit is configured to measure the reflection characteristics of the object based on object information and instruction information, the object information is information including the coordinate position relationship between the light source position of the incident light, the light detection position of the reflected light, and the measurement part in the object, and numerical values ​​related to the incident light and the reflected light, the incident light is light irradiated to the measurement part, the reflected light is light reflected from the measurement part after the incident light is irradiated to the measurement part, the instruction information is information related to the known measurement results of the reflection characteristics, and the number of combinations of the coordinate position relationship included in the object information is 1 to 15.

Description

Technical Field

[0001] The present invention relates to a device, a system, a program and a method for measuring reflection characteristics. Background Art

[0002] As a component of the visual texture of an object, "reflection characteristics" are very important. Reflection characteristics play an important role in fields such as data archiving and quality management. When measuring reflection characteristics, sampling is performed by irradiating incident light to an object and detecting its reflected light. The reflection characteristics are expressed as a function, which is estimated from the values ​​sampled in the space formed by the variables. For example, non-patent document 1 discloses a goniometer capable of measuring reflection characteristics. A goniometer is a measuring device composed of a light source and a light detector, and is widely used because of its feature of being able to measure values ​​in any sampling direction.

[0003] Prior art literature

[0004] Non-patent literature

[0005] Non-patent document 1: Sing Choong Foo, “A gonioreflectometer for measuring the bidirectional reflectance of material for use in illumination computation”, 1997. Summary of the invention

[0006] However, when using a measuring device such as a goniometer to measure reflective properties, it is necessary to sample the incident light / reflected light from a large number of directions. For example, the relatively simple isotropic bidirectional reflectance distribution function (BRDF) in reflective properties has three degrees of freedom angle parameters, but if each parameter is sampled once, it will be a huge number of samples of 90×90×180=1,458,000, and the long measurement time will also become a problem. Therefore, it can be said that the time required to measure reflective properties needs to be further shortened.

[0007] In view of the above situation, the present invention aims to provide a measuring device, a measuring system, a measuring program and a measuring method which can measure the reflection characteristics of a desired object quickly and with high accuracy with a small number of samples.

[0008] According to one embodiment of the present invention, there is provided a device for measuring reflection characteristics, characterized in that it comprises a control unit, wherein the control unit is configured to measure the reflection characteristics of an object based on object information and instruction information, the object information is information including a light source position of incident light, a light detection position of reflected light, and a coordinate position relationship of a measurement portion in the object, and numerical values ​​related to the incident light and the reflected light, the incident light is light irradiated to the measurement portion, the reflected light is light reflected from the measurement portion after the incident light is irradiated to the measurement portion, the instruction information is information related to a known measurement result of the reflection characteristics, and the number of combinations of the coordinate position relationship contained in the object information is 1 to 15.

[0009] The measuring device of the present invention measures the reflection characteristics of an object based on information (object information) including the coordinate position relationship (sampling direction) of the light source position, light detection position and measurement part, numerical values ​​(sampling values) of incident light and reflected light, and information (command information) related to known measurement results of reflection characteristics. It should be noted that the number of combinations (sampling number) of coordinate position relationships included in the object information is 1 to 15. The measuring device having such a structure can achieve the effect of measuring the desired reflection characteristics in a very short time. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 This is a schematic diagram of the structure of the measurement system involved in this embodiment.

[0011] Figure 2 It is a functional module diagram of the control unit in the measuring device involved in this embodiment.

[0012] Figure 3A and Figure 3B is a diagram representing the Rusinkiewicz coordinate system.

[0013] Figure 4 A schematic diagram showing a neural network.

[0014] Figure 5 This is an example diagram showing that the number of samples is 6.

[0015] Fig. 6A To express Figure 5 A graph showing the measurement results of the reflection characteristics for the case shown, Figure 6B To express Figure 5 A graph of the true value of the reflectance characteristic for the case shown.

[0016] Figure 7 This is an example diagram showing that the number of samples is 3.

[0017] Fig. 8A To express Figure 7A graph showing the measurement results of the reflection characteristics for the case shown, Figure 8B To express Figure 7 A graph of the true value of the reflectance characteristic for the case shown.

[0018] Fig. 9 It is a flow chart showing the measurement method involved in this embodiment.

[0019] Fig. 10A This is a graph showing the reflectivity distribution of an object, that is, the glossiness distribution (equivalent to reflectivity). Fig. 10B A diagram showing the BRDF distribution of an object.

[0020] Fig.11A This is an example of measuring the glossiness of an object (white and achromatic) to estimate the Full BRDF and outputting it as a computer graphic (Stanford bunny) of a different object. Fig. 11B This is an example of measuring the glossiness of an object (including both white and non-white achromatic colors) to estimate the Full BRDF and outputting it as a computer graphic (Stanford bunny) of a different object. Fig. 11C This is an example of measuring the glossiness of an object (non-white and achromatic) to estimate the Full BRDF and outputting it as a computer graphic (Stanford bunny) of a different object.

[0021] Fig.12 This is an example of measuring the glossiness of an object (with color) to estimate the Full BRDF and outputting it as a computer graphic of a different object (Stanford bunny) including color information. DETAILED DESCRIPTION

[0022] The embodiments of the present invention will be described below in conjunction with the accompanying drawings. The various features in the embodiments shown below can be combined with each other. In particular, the "unit" in this specification can be a concept that includes, for example, a combination of hardware resources implemented by a circuit in a broad sense and software information processing specifically implemented by these hardware resources. In addition, various information is involved in this embodiment, and this information is represented by the high and low signal values ​​as a binary bit set composed of 0 or 1, and communication and calculation can be performed on a circuit in a broad sense.

[0023] In addition, a circuit in a broad sense is a circuit realized by at least appropriately combining circuits, circuit types, processors, and memories, etc. That is, it includes application-specific integrated circuits (ASICs), programmable logic devices (such as simple programmable logic devices (SPLDs), complex programmable logic devices (CPLDs)), and field programmable gate arrays (FPGAs).

[0024] 1. Overall structure

[0025] Figure 1 FIG. 1 is a schematic diagram of the structure of the measurement system 1 involved in this embodiment. The measurement system 1 is configured to measure the reflection characteristics of the object S. Figure 1 As shown, the measurement system 1 includes a measurement device 2 and an information processing device 3, which are connected to each other via a network. In Sections 1.1 to 1.2, the measurement device 2 and the information processing device 3 will be described separately.

[0026] 1.1 Measurement device 2

[0027] The measuring device 2 includes a communication unit 21, a storage unit 22, a control unit 23, a light source 24, a light detection unit 25, and a display unit 26, and these components are electrically connected inside the measuring device 2 via a communication line 20. Each component will be further described below.

[0028] <Communication Department 21>

[0029] The communication unit 21 preferably uses wired communication methods such as USB, IEEE1394, Thunderbolt, and wired LAN network communication, but may also include wireless LAN communication, mobile communication such as LTE / 3G, Bluetooth (registered trademark) communication, etc. as needed. That is, it is preferably implemented by combining multiple communication methods. In addition, the reflection characteristics can be measured while communicating with the information processing device 3 described later as an external device via the communication unit 21, or independently operated in an offline environment. In addition, the measured reflection characteristics can also be sent to the information processing device 3.

[0030] <Storage Unit 22>

[0031] The storage unit 22 (an example of a "storage medium" in the scope of the claims) is used to store the defined information. For example, the storage unit 22 stores object information, instruction information, and various programs for the control unit 23 to execute. This is implemented by a storage device such as a solid state drive (SSD) or a memory such as a random access memory (RAM) that stores temporary necessary information (parameters, arrays, etc.) related to program operations. In addition, various combinations of these are possible.

[0032] The object information is sampling data required for measuring the reflection characteristics of the object S, and is defined by the coordinate position of the light source 24 (incident light), the light detection unit 25 (reflected light), and the measurement position Sp of a part of the object S described later, and numerical values ​​(radiation intensity, etc.) related to the incident light and the reflected light. In addition, the instruction information is information about the known measurement results of the reflection characteristics, and is instruction data used when the reflection characteristics are estimated using the machine learning described later. In general, the instruction information can be, for example, information including various parameters machined based on the known measurement results, but can also be information including the measurement results themselves. In addition, when the measuring device 2 is implemented, the instruction information can be stored in the storage unit 22 in advance, but the update data of the instruction information can also be downloaded from the information processing device 3 via the communication unit 21 as needed and stored. In addition, the updated data of the downloaded instruction information can be all or part of the instruction data stored in the information processing device 3.

[0033] <Control Unit 23>

[0034] The control unit 23 performs processing and control of the overall operation related to the measuring device 2. The control unit 23 is, for example, a central processing unit (CPU) not shown. The control unit 23 realizes various functions related to the measuring device 2 by reading a predetermined program stored in the storage unit 22. Figure 1 Although a single control unit 23 is shown in the figure, it is not limited to this, and it can also be implemented by multiple control units 23 (dedicated chips, etc.) according to the function. In addition, these can also be combined in various ways.

[0035] Figure 2It is a functional block diagram that details the functions of the control unit 23. The control unit 23 has a light source lighting unit 231, a reflection characteristic estimation unit 232, and a rendering unit 233. The light source lighting unit 231 lights the light source 24 as needed when measuring the reflection characteristic. The reflection characteristic estimation unit 232 measures the reflection characteristic of the measurement part Sp based on the object information as sampling data and the instruction information stored in the storage unit 22. More specifically, the reflection characteristic estimation unit 232 measures the reflection characteristic based on machine learning through a neural network. The machine learning inputs the object information, uses the instruction information as instruction data, and outputs the reflection characteristic (refer to Section 3). Then, the reflection characteristic of the object S is estimated by preparing a plurality of such object information. The rendering unit 233 can generate the computer graphics by performing rendering of the computer graphics of the object S having the reflection characteristic.

[0036] <Light Source>

[0037] The light source 24 is configured to irradiate the incident light L_i to the measurement part Sp which is a part of the object S. The incident light L_i is preferably a general diffuse white light (containing at least RGB components). Based on the lighting command signal of the light source lighting unit 231, the light source 24 is lit via a lighting circuit not shown. In addition, the incident light-related value as a parameter in the object information is not particularly limited, and a value pre-stored in the storage unit 22 can be used.

[0038] <Light Detection Unit 25>

[0039] The light detection unit 25 is an element that detects light and converts it into an electrical signal, and includes, for example, a photodiode, a photomultiplier tube, a photoconductive element, a CCD, a camera, etc. Here, the light detection unit 25 is configured to detect the reflected light L_o that is reflected by the incident light L_i being irradiated to the measurement site Sp that is a part of the object S. The detected reflected light L_o is converted into an electrical signal and stored in the storage unit 22 as a parameter of the object information, that is, numerical information about the reflected light.

[0040] <Display section 26>

[0041] The display unit 26 is a display that presents information by stimulating the user's vision. Of course, on the basis of the display unit 26, other sensory-related information presentations such as speakers (not shown) and vibrators (not shown) can also be added in combination. More specifically, it can be emphasized in a multi-peak or cross-peak manner according to the purpose. Through such a structure, the texture of the object S can be virtually presented to the observer, and it can also be expanded to a "comprehensive texture presentation system". In particular, the display unit 26 can represent the measured reflection characteristics through the control unit 23 based on the object information and the instruction information. In more detail, a computer graphic of the object S having the said reflection characteristics can be represented. Furthermore, such computer graphics can also be implemented as printing, color projection or projection mapping on various objects.

[0042] 1.2 Information processing device 3

[0043] like Figure 1 As shown, the information processing device 3, that is, the workstation (Work Station), has a storage unit 31 and a control unit 32. Of course, wired communication methods such as USB, IEEE1394, Thunderbolt, wired LAN network communication, or wireless communication methods such as wireless LAN network communication, LTE / 3G and other mobile communications and Bluetooth (registered trademark) can be used as needed. Communication with the measuring device 2 can be achieved via such communication methods. The following will further explain the various components involved in the information processing device 3.

[0044] <Storage Unit 31>

[0045] The storage unit 31 (an example of a "storage medium" in the scope of the claims) is used to store the instruction information described in Section 1.1 and various programs for the control unit 32 to execute. It can be implemented as a storage device such as a hard disk drive (HDD) or a solid state drive (SSD). Of course, it can also be implemented as a memory such as a random access memory (RAM) that stores temporary necessary information (parameters, arrays, etc.) involved in program calculations, and it is preferred to combine these in various ways.

[0046] Although not particularly limited, the instruction information stored in the storage unit 31 is preferably configured to allow data to be added. In this way, the measurement device 2 connected to the information processing device 3 via the network can obtain the latest instruction information with richer information. That is, it can be expected that the accuracy of the reflection characteristics measured by the control unit 23 in the measurement device 2 can be improved.

[0047] <Control Unit 32>

[0048] The control unit 32 performs processing and control of the overall operation related to the information processing device 3. The control unit 32 is, for example, a central processing unit (CPU) not shown. The control unit 32 implements various functions related to the information processing device 3 by reading a prescribed program stored in the storage unit 31. In this embodiment, detailed description of these functions will be omitted.

[0049] 2. Reflective characteristics

[0050] In Section 2, an example of reflection characteristics measured by the measurement system 1 according to this embodiment will be described in detail.

[0051] First, as a function expressing the reflection characteristics, there is a bidirectional scattering surface reflectance distribution function (BSSRDF), which is specifically expressed as [Formula 1].

[0052] [Formula 1]

[0053]

[0054] Here, E_i, x_i, w_i( and θ_i) are the irradiance, incident position, and incident direction of the incident light L_i. In addition, L_o, x_o, ω_o ( and θ_o) are the radiance, reflection position, and reflection direction of the reflected light L_o. Those in the BSSRDF that do not depend on the incident position and the reflection position are specifically called bidirectional reflectance distribution function (BRDF) and are expressed as [Formula 2].

[0055] [Formula 2]

[0056]

[0057] As a method of obtaining the BRDF coordinate system, in addition to the coordinate system based on the directions of the incident light L_i and the reflected light L_o as described above, the Rusinkiewicz coordinate system based on the half-vector definition is also known. The half-vector ω_h is expressed as [Formula 3].

[0058] [Formula 3]

[0059]

[0060] Here, it is assumed that the isotropy is constant with respect to the rotation about the normal n, and the degree of freedom of ω_h is 1. As shown in [Formula 4], θ_h, θ_d, As a function of three variables. However, if Figure 3A and 3B As shown, each variable is defined using the Rusinkiewicz coordinate system.

[0061] [Formula 4]

[0062] f BRDF (θ h ,θ d ,φ d )

[0063] Although anisotropic reflection is found in materials such as grained metals, jewelry, and textiles, it is assumed to be isotropic here for simplicity. That is, in this embodiment, it is assumed that the object S is a material whose reflection characteristics are expressed by an isotropic BRDF, and the influence of subsurface scattering can be ignored. In addition, the function shown in [Formula 4] can be simply described as f(θ_h, θ_d, ). It should be noted that this is merely an example in the present embodiment, and the present invention is not limited thereto.

[0064] 3. Machine Learning Using Neural Networks

[0065] In Section 3, machine learning using a neural network (an example of "second machine learning" in the claims) will be described in detail. In the measurement of the reflection characteristics involved in this embodiment, a method of reducing the number of samples (an example of "number of combinations" in the claims) is adopted to achieve high-speed reflection characteristic measurement. This is called the minimum sampling method. In addition, in order to achieve high speed, the sampling direction is not adaptively determined, but a predetermined direction is used. Therefore, its advantage is that no movable part is required in the structure of the measuring device 2.

[0066] When the minimum sampling method is used, it is necessary to estimate the data of the unsampled direction, so improving the accuracy becomes a problem. In this embodiment, an estimator (reflection characteristic estimator 232) is provided that uses a neural network to input sampled data and output reflection characteristics. By designing such an estimator, model generation and model parameter estimation can be optimized at the same time, thereby improving performance.

[0067] Furthermore, since the reflection characteristic model for a specific sampling direction is learned, there is no sampling direction for which the reflection characteristic model is good or bad, and the estimator can be freely constructed with respect to the sampling direction. Therefore, the function of the existing gloss meter can be expanded to provide a more compact measurement device 2. This will be described in detail in Section 6.

[0068] Here, in order to improve the estimation accuracy, the reflection characteristic estimation unit 232 introduces a conversion called cos-mapping represented by [Formula 5].

[0069] [Formula 5]

[0070]

[0071] The conversion is used to satisfy the inverse of Helmholtz. In addition, θ_h->sinθ_h and θ_d->cosθ_d are conversions for more detailed examination of specular reflection near θ_h=0 and Fresnel reflection near θ_d=π / 2. Based on such conversion, it can be considered that the accuracy of machine learning can be improved through neural networks.

[0072] Figure 4 It is a schematic diagram of the neural network NN. The input signal specified by various parameters is input to the first layer L1. Here, the input signal is object information including the sampling direction (an example of "coordinate position relationship" in the scope of the claims) and the sampling value (an example of "values ​​related to incident light and reflected light" in the scope of the claims). Such input signals are output from the calculation nodes N_11 to N_13 of the first layer L1 to the calculation nodes N_21 to N_25 of the second layer L2, respectively. At this time, the value output from the calculation nodes N_11 to N_13 is multiplied by the weight w set between each calculation node N, and the value obtained is input to the calculation nodes N_21 to N_25.

[0073] The calculation nodes N_21 to N_25 add the input values ​​from the calculation nodes N_11 to N_13 and input such values ​​(or such values ​​plus a predetermined bias value) to a predetermined activation function. As the activation function, for example, a function as shown in [Formula 6] is used.

[0074] [Formula 6]

[0075]

[0076] Then, the output value of the activation function is propagated to the calculation node N_31 which is the next node. At this time, the value obtained by multiplying the weight w set between the calculation nodes N_21 to N_25 and the calculation node N_31 by the output value is input to the calculation node N_31. The calculation node N_31 adds the input values ​​and outputs the total value as an output signal. At this time, the calculation node N_31 can add the input values, and input the value obtained by adding the bias value to the total value to the activation function, and output the output value as an output signal. Thus, the estimated BRDF is output.

[0077] It should be noted that Figure 4The content shown is for explanation purposes only and is not limited thereto. For example, in the present embodiment, a method with two intermediate layers can also be adopted. The number of nodes in the intermediate layer is an adjustable parameter, such as (64, 2048), (128, 1024), (256, 512), (512, 256), (1024, 128), (2048, 64), etc., among which (128, 1024), (512, 256) are preferred, and (128, 1024) is more preferred. In addition, as the instruction information of the measurement result of the known reflection characteristic, it is preferred to use the information contained in the appropriate database that collects the isotropic BRDF.

[0078] 4. Measurement

[0079] By using the measurement system 1 as described above, the number of samples can be reduced, and the BRDF, i.e., the reflective characteristics, can be measured faster and with higher accuracy than before. The number of samples is, for example, 1 to 15, preferably 2 to 10, and even more preferably 3 to 6. Specifically, for example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or in the range between any two values ​​exemplified here. For example, Figure 5 As shown, when the sampling number is 6, the following can be obtained: Fig. 6A The results are shown. It should be noted that Figure 6B is a preliminary measure of the true value of the result. This measurement uses the following sampling direction. However, θ_i, and θ_o is Figure 3A and Figure 3B As shown, and is expressed in degrees.

[0080]

[0081] This combination refers to a known industrial standard, in which the light source 24 is located at three different positions, and the light detection unit 25 (i.e., the detection position of the reflected light) is located at four different positions (respectively Figure 5 Of course, this is only an example and is not limited to this. Although the light source 24 and the light detection unit 25 are preferably prepared separately to eliminate the movable parts, the movable parts may be provided so that one light source 24 and the light detection unit 25 can be reused. Fig. 6A and Figure 6B There is no obvious difference between the two, and the purpose of measuring reflection characteristics at high speed and high accuracy has been achieved.

[0082] Furthermore, if Figure 7 As shown, when the sampling number is 3, the following can be obtained: Fig. 8A The results are shown. It should be noted that Figure 8B is a preliminary measure of the true value of this result. This measurement uses the following sampling direction. However, θ_i, and θ_o is Figure 3A and Figure 3B As shown, and is expressed in degrees.

[0083]

[0084] Such a combination is independently set according to the industrial standard. The position of the light source 24 is set to three different positions, and the position of the light detection unit 25 (i.e., the detection position of the reflected light) is one position (respectively at Figure 7 Of course, this is only an example and is not limited to this. Although the light source 24 and the light detection unit 25 are preferably prepared separately to eliminate the movable part, the movable part may be provided so that one light source 24 and the light detection unit 25 can be reused. Fig. 8A and Figure 8B There is no obvious difference between the two, and the purpose of measuring reflection characteristics at high speed and high accuracy has been achieved.

[0085] 5. Measurement method using measurement device 2

[0086] In Section 5, an example of a measurement method for measuring the reflection characteristics of an object S using the measurement device 2 described so far will be discussed. Fig. 9 is a flow chart of this measurement method. Fig. 9 Each step shown is explained in sequence.

[0087] [start]

[0088] (Step S1)

[0089] In step S1 (an example of a "light irradiation step" in the scope of claims), in the measuring device 2, the light source lighting unit 231 in the control unit 23 reads a prescribed program stored in the storage unit 22. Then, the light source 24 is lit with a prescribed intensity, so that the incident light L_i is irradiated to the measurement site Sp in the object S. The coordinate position and prescribed intensity of the light source 24 are stored in the storage unit 22.

[0090] (Step S2)

[0091] In step S2 (an example of a "light detection step" in the scope of the claims), the incident light L_i irradiated to the measurement site Sp in step S1 is reflected, and the light detection unit 25 detects the reflected light L_o at a predetermined intensity. The coordinate position of the light detection unit 25 and the measurement site Sp and the detected predetermined intensity are stored in the storage unit 22.

[0092] (Step S3)

[0093] Step S3 (an example of a "measurement step" in the scope of the claims) is that the reflection characteristic estimation unit 232 in the control unit measures the reflection characteristic of the object S based on the information stored in the storage unit 23 (refer to the steps S1 and S2), that is, the object information, and the instruction information also pre-stored in the storage unit 23. More specifically, the reflection characteristic is estimated and measured by machine learning through a neural network. At this time, the number of combinations of coordinate position relationships including the object information is smaller than that of the conventional reflection characteristic measurement method, for example, it is set in the range of 1 to 15 or less.

[0094] [Finish]

[0095] 6. Modifications

[0096] In addition, this embodiment can be further creatively planned through the following implementation methods.

[0097] First, the reflection characteristics obtained from the measurement results can be applied as an example to an object different from the object S. In other words, the control unit 32 in the information processing device 3 performs rendering of computer graphics of different objects to have the reflection characteristics of the object S. That is, it is expected to be applied to the field of animation content production, such as applying the reflection characteristics measured in advance to the desired object. This will be described in detail in Section 7 using the existing gloss meter and colorimeter as an example.

[0098] Second, the function related to the control section 23 in the measuring device 2 (more specifically, the reflection characteristic estimation section 232 and the rendering section 233) can be executed so that the control section 32 in the information processing device 3 has the function. That is, through the communication section 21 in the measuring device 2, the object information is sent from the communication section 21 in the measuring device 2 to the information processing device 3 via the network, and the control section 32 in the information processing device 3 can measure the reflection characteristic based on such object information and the instruction information stored in the storage section 31.

[0099] Third, the object information acquired in advance can be read into the measuring device 2 afterwards to measure the reflection characteristics of the object S. In this case, the object information can be sent to the measuring device 2 via the communication unit 21 described in Section 1, or can be read into the measuring device 2 via a so-called flash memory (e.g., an SD memory card, a USB memory, a storage disk, a smart medium, a CF card, etc.). In addition, as the object information, the parameters contained in the object information can be read, and the data indirectly containing these parameters (e.g., an image file, etc.) can also be read. Of course, it should be noted in this case that the light source 24 and the light detection unit 25 are not essential components in the measuring device 2.

[0100] 7. Embodiments using a gloss meter or a colorimeter

[0101] In this section, an embodiment in which a gloss meter or a colorimeter is used as the measuring device 2 will be described in detail.

[0102] 7.1 Estimation of Monochromatic BRDF Using Gloss Meter

[0103] The measuring device 2 (gloss meter) measures the "glossiness" of the object S and then converts it into the "BRDF" of the reflection characteristic. According to the JIS standard, on a glass surface with a refractive index of 1.567, when the incident angle is 60 degrees, the reflectivity of 10% is defined as the glossiness of 100%; when the incident angle is 20 degrees, the reflectivity of 5% is defined as the glossiness of 100%. That is, it should be noted that the glossiness is a physical quantity that depends on the reflectivity. Therefore, theoretically, the BRDF as the reflection characteristic can be calculated in a linear relationship based on the known glossiness of the object S.

[0104] Fig. 10A and 10B The graph of FIG. 1 shows the glossiness of the object S measured by the measuring device 2 (gloss meter) (as described above, since the glossiness is a physical quantity that depends on the reflectivity, Fig. 10AThe vertical axis corresponds to glossiness, that is, it can be replaced by glossiness), and the distribution of BRDF is displayed. Here, the distribution of reflected light L_o for each reflection angle (dependence of the reflection angle on the receiving angle) is shown when the incident light L_i is incident at 20 degrees. In addition, A to E of each graph respectively relate to the case where the object S is leather A, medium gloss plastic B, low gloss plastic C, high gloss brick D and low gloss brick E. Through such results, it can be confirmed that the breadth of the BRDF distribution is wider than the breadth of the gloss distribution measured by the glossmeter. This is inferred based on the difference between the light detection unit 25 in the measuring device (glossmeter) and the sensor (not shown) that measures the reflection characteristics (BRDF) as instruction information. Therefore, machine learning headed by support vector machine (SVM) and the like (an example of "first machine learning" in the scope of claims) is used to learn the conversion from glossiness to BRDF (an example of "partial reflection characteristics" in the scope of claims), and on this basis, creative planning is made to obtain the distribution of BRDF. In particular, when the breadth of the distribution is taken into consideration, it is preferable to generate, for example, a method that can be obtained from Fig. 10A The gloss distribution of 15 to 25 degrees on the horizontal axis is estimated Fig. 10B Machine learning model of the BRDF values ​​from 5 to 35 degrees on the horizontal axis.

[0105] That is, glossiness is input, and the conversion with BRDF as output (as described in this section) is combined with the conversion with BRDF as input and the overall BRDF (Full BRDF) of the object S as output (which is described as a high-level conceptualization of "reflection characteristics" in Sections 1 to 5), thereby measuring glossiness and obtaining the overall BRDF (FullBRDF). As described in the modification example, it can be applied to the rendering of computer graphics of different objects to have the BRDF of the object S.

[0106] Figures 11A to 11C The glossiness of the object S is measured, and the full BRDF (Full BRDF) is estimated and output as computer graphics (Stanford bunny) of different objects. Since the gloss meter cannot obtain color information, various colorless experiments are performed. In any case, the full BRDF (Full BRDF) can be estimated from the glossiness of the object S, and it can be output as a Stanford bunny suitable for computer graphics.

[0107] 7.2 Color Information Restoration Using a Colorimeter

[0108] exist Fig. 10AIn the figure, when referring to, for example, the vicinity of 20 degrees on the horizontal axis, each object S formed by each material A to E has a reflection peak at each position due to natural regular reflection (specular reflection). On the other hand, when referring to the vicinity of 15 degrees, it is confirmed that the glossiness of each object S made of each material A to E has dropped to a value close to 0. That is, in the 15-degree area (diffuse reflection component), the gloss is almost invisible like the 20-degree area (specular reflection component), but people perceive it as a color. Therefore, as described above, if it is possible to generate a color that can be based on Fig. 10A The gloss distribution of 15 to 25 degrees on the horizontal axis is estimated Fig. 10B The machine learning model of the BRDF values ​​between 5 and 35 degrees on the horizontal axis is expected to be able to restore color information when generating computer graphics.

[0109] Therefore, the experiment was conducted again using the measuring device 2 as a gloss meter and a colorimeter, and the results are shown in Fig.12 As shown. The glossiness of the object S is measured by using the measuring device 2 (gloss meter), and the Stanford bunny whose monochromatic reflection characteristics are restored using the overall BRDF (Full BRDF) estimated thereby is output. From this, the diffuse reflection component is extracted, and by applying an existing algorithm related to computer graphics, the color measured by the measuring device 2 (colorimeter) is output as a Stanford bunny including the color information of the object S.

[0110] 7.3 Summary

[0111] Thus, by combining the existing gloss meter and colorimeter to constitute the measuring device 2, rendering of computer graphics of various objects can be performed to have the reflection characteristics of the object S. It should be noted that this is only an experiment, and a new experiment of the measuring device 2 capable of simultaneously measuring both glossiness and color is more preferred.

[0112] That is, the following points should be noted. The control unit 23 is configured to measure the reflection characteristics based on the glossiness of the object S. The control unit 23 estimates and measures the partial reflection characteristics as a part of the reflection characteristics through the first machine learning with the glossiness input, and estimates and measures the reflection characteristics through the second machine learning with the partial reflection characteristics input. The reflection characteristics include the specular reflection (e.g., regular reflection) component representing the gloss and the diffuse reflection component associated with the color.

[0113] 8. Conclusion

[0114] As described above, according to the present embodiment, it is possible to implement the measuring device 2 that can measure the reflection characteristics of a desired object at a higher speed and with a higher accuracy than before.

[0115] The measuring device 2 has a control unit 23, and the control unit 23 is configured to measure the reflection characteristics of the object S based on object information and command information. The object information is information including the light source position of the incident light L_i, the light detection position of the reflected light L_o, the coordinate position relationship of the measurement part Sp in the object S, and numerical values ​​related to the incident light L_i and the reflected light L_o. The incident light L_i is the light irradiated to the measurement part Sp, and the reflected light L_o is the light that is irradiated to the measurement part Sp by the incident light L_i and then reflected from the measurement part Sp. The command information is information related to the known measurement results of the reflection characteristics. The number of combinations of the coordinate position relationship included in the object information is 1 to 15.

[0116] In addition, it is possible to implement the measurement system 1 capable of measuring the reflection characteristics of a desired object at a higher speed than before.

[0117] The measuring system 1 includes a measuring device 2 and an information processing device 3, the measuring device 2 includes a light source 24 and a light detection unit 25, the light source 24 irradiates incident light L_i to a measuring portion Sp of an object S, the light detection unit 25 is configured to detect reflected light L_o that is irradiated to the measuring portion Sp by the incident light L_i and then reflected from the measuring portion Sp, at least one of the measuring device 2 and the information processing device 3 further includes a control unit 23 / 32, the control unit 23 / 32 is configured to measure the reflection characteristics of the object S based on object information and command information, the object information is information including a coordinate position relationship among the light source 24, the light detection unit 25 and the measuring portion Sp, and numerical values ​​related to the incident light L_i and the reflected light L_o, the command information is information related to known measurement results of the reflection characteristics, the number of combinations of the coordinate position relationship included in the object information is 1 to 15, and the measuring device 2 and the information processing device 3 are configured to send and receive at least one of the object information, the command information and the information including the reflection characteristics to each other via a network.

[0118] Then, the software that implements the measuring device 2 capable of measuring the reflection characteristics of the desired object at a higher speed than before as hardware can also be implemented as a program. Therefore, such a program can be provided as a non-transitory computer readable medium, can be downloaded from an external server, and can be started by an external computer to execute the so-called cloud computing that can realize various functions on the client terminal.

[0119] This measurement program enables the computer to execute a specified function, and the specified function has a measurement function. Through the measurement function, the reflection characteristics of the object are measured based on object information and instruction information. The object information is information including the light source position of the incident light L_i, the light detection position of the reflected light L_o, the coordinate position relationship of the measurement part Sp of the object S, and the numerical values ​​of the incident light L_i and the reflected light L_o. The incident light L_i is the light irradiated to the measurement part Sp, and the reflected light L_o is the light reflected from the measurement part Sp after the incident light L_i is irradiated to the measurement part Sp. The instruction information is information related to the known measurement results of the reflection characteristics. The number of combinations of the coordinate position relationship contained in the object information is 1 to 15.

[0120] Furthermore, by using the measuring device 2 and the measuring system 1 , it is possible to implement a measuring method for measuring the reflection characteristics of a desired object at a higher speed and with higher accuracy than before.

[0121] This measurement method includes a light irradiation step S1, a light detection step S2 and a measurement step S3. The light irradiation step S1 irradiates incident light L_i to a measurement portion Sp of an object S. The light detection step S2 detects reflected light L_o that is irradiated by the incident light L_i to the measurement portion Sp and then reflected from the measurement portion Sp. The measurement step S3 measures the reflection characteristics of the object S based on the light source position of the incident light L_i, the detection position of the reflected light L_o and the coordinate position relationship of the measurement portion Sp, numerical values ​​related to the incident light L_i and the reflected light L_o, and known measurement results of the reflection characteristics. The number of combinations of the coordinate position relationship is 1 to 15.

[0122] Finally, although various embodiments of the present invention have been described, these embodiments are only presented as examples and are not intended to limit the scope of the present invention. The present invention may also be implemented through various other embodiments. Any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention shall be included in the protection scope of the present invention. The relevant embodiments or variations shall not only be included in the scope or gist of the present invention, but also in the invention described in the claims and their equivalents.

[0123] Simple description of symbols

[0124] 1: Measurement system

[0125] 2: Measuring device

[0126] 20: Communication loop

[0127] 21: Ministry of Communications

[0128] 22: Storage

[0129] 23: Control Department

[0130] 231: Light source lighting department

[0131] 232: Reflection characteristic estimation unit

[0132] 233: Rendering Department

[0133] 24: Light Source

[0134] 24a: Light Source

[0135] 24b: Light Source

[0136] 24c: Light source

[0137] 24d: Light source

[0138] 24e: Light Source

[0139] 24f: Light source

[0140] 25: Light detection unit

[0141] 25a: Light detection unit

[0142] 25b: Light detection unit

[0143] 25c: Light detection unit

[0144] 25d: Light detection unit

[0145] 26: Display Department

[0146] 3: Information processing device

[0147] 31: Storage

[0148] 32: Control Department

[0149] L_i: incident light

[0150] L_o: reflected light

Claims

1. A device for measuring reflection characteristics, characterized in that: Equipped with a control unit, The control unit is configured to estimate the reflection characteristics of the measurement object by inputting object information into a learned model that has been previously machine-learned. The object information includes information on the coordinate position relationship between the light source position of the incident light, the light detection position of the reflected light, and the measurement position in the object; and information on numerical values ​​related to the incident light and the reflected light. The incident light is light irradiated to the measurement site, and the reflected light is light that is irradiated to the measurement site by the incident light and then reflected from the measurement site. The learned model is a model that has learned the relationship between object information in existing measurements and reflection characteristics measured using at least the object information as a sample, The number of combinations of the coordinate position relationship included in the object information is 1 to 15; The reflection characteristics are expressed as θ_h, θ_d, Function of a variable In the machine learning, it includes Transformed to The process of transforming θ_h into sinθ_h and θ_d into cosθ_d; where the reflection characteristic is invariant with respect to rotation about the normal n and the half vector ω h When the degree of freedom is 1, the reflection characteristic is expressed as Among them, the half vector ω h for Among them, w i represents the incident direction of the incident light, ω o Indicates the reflection direction of the reflected light.

2. The measuring device according to claim 1, characterized in that: It also has a light source and a light detection unit. The light source irradiates the incident light, and the light detection unit is configured to detect the reflected light.

3. The measuring device according to claim 1, characterized in that: The device further includes a communication unit configured to output at least one of the object information and the information including the reflection characteristic to an external device.

4. The measuring device according to claim 1, characterized in that: The control unit is configured to generate computer graphics based on the reflection characteristics.

5. The measuring device according to claim 4, characterized in that: The control unit is configured to apply the reflection characteristic to an object different from the target object and generate a computer graphic of the object.

6. The measuring device according to claim 1, characterized in that: The number of combinations is 2-10.

7. The measuring device according to claim 6, characterized in that: The number of combinations is 3-6.

8. The measuring device according to claim 1, characterized in that: The control unit is configured to measure the reflection characteristic based on the glossiness of the object.

9. The measuring device according to claim 8, characterized in that: The control unit estimates and measures a partial reflection characteristic that is a part of the reflection characteristic by first machine learning to which the glossiness is input, The reflection characteristic is estimated and measured by the second machine learning which inputs the partial reflection characteristic.

10. The measuring device according to claim 1, characterized in that: The reflection characteristics include a specular reflection component showing gloss and a diffuse reflection component associated with color information.

11. A computer-readable non-transitory storage medium having a measurement program stored thereon, characterized in that: The measuring program causes a computer to function as the measuring device according to claim 1 .

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