Three-dimensional identification processing device, optical inspection device, three-dimensional identification processing method, and three-dimensional identification processing program

Abstract

To provide a three-dimensional identification processing device capable of acquiring normal information to an object surface using an image capturing device capable of acquiring depth information of objects.SOLUTION: A three-dimensional identification processing device according to an embodiment comprises a processor configured to perform depth variation computation processing for deriving depth variations with positional changes on an image on an image with depth information acquired using an image capturing device with at least two pixels capable of acquiring depth information of objects, and acquire normal information of a surface of the object.SELECTED DRAWING: Figure 2

Description

[Technical Field]

[0001] An embodiment of the present invention relates to a three-dimensional identification processing device, an optical inspection device, a three-dimensional identification processing method, and a three-dimensional identification processing program. [Background technology]

[0002] Three-dimensional measurement of objects is required in various industries. One such measurement method is the ToF (Time of Flight) camera, which measures depth distance from the flight time of light. The data that can be acquired by a ToF camera is point cloud data. [Prior art documents] [Non-patent literature]

[0003] [Non-Patent Document 1] Qianjin Yuan, Yong Luo, HeShan Wang, “3D point cloud recognition of substation equipment based on plane detection,” Results in Engineering, Volume 15, September 2022, 100545 Summary of the Invention [Problem to be solved by the invention]

[0004] The problem that the present invention aims to solve is to provide a three-dimensional identification processing device, an optical inspection device, a three-dimensional identification processing method, and a three-dimensional identification processing program that are capable of acquiring normal information of an object surface using an imaging device that can acquire depth information of the object. [Means for solving the problem]

[0005] According to an embodiment, the three-dimensional identification processing device has a processor that performs a depth change calculation process on an image containing depth information acquired using an imaging device that has at least two pixels and can acquire depth information of an object, to calculate a change in depth in response to a change in position on the image, and acquires normal information of the object's surface. [Brief explanation of the drawings]

[0006] [Figure 1] 1 is a schematic diagram of an optical inspection device (three-dimensional identification device) according to a first embodiment. [Figure 2] FIG. 10 is a flowchart illustrating a series of steps for outputting a normal vector by a processing device. [Figure 3] FIG. 10 is a schematic diagram of an optical inspection device (three-dimensional identification device) according to a second embodiment. [Figure 4] FIG. 10 is a diagram showing an application example in which STL data is calculated by a processing device based on point cloud data acquired by an optical inspection device according to a second embodiment. [Figure 5] FIG. 10 is a schematic diagram of an image sensor of an optical inspection device (three-dimensional identification device) according to a third embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0007] Each embodiment will be described below with reference to the drawings. The drawings are schematic or conceptual, and the relationship between the thickness and width of each part, the size ratio between parts, etc., are not necessarily the same as those in reality. Furthermore, even when the same part is shown, the dimensions and ratios may be different depending on the drawing. In this specification and each drawing, elements similar to those previously described with reference to the previous drawings are designated by the same reference numerals, and detailed explanations will be omitted where appropriate.

[0008] In this specification, light is a type of electromagnetic wave, and includes X-rays, ultraviolet light, visible light, infrared light, microwaves, etc. In this embodiment, the light is considered to be visible light, with a wavelength in the range of 400 nm to 750 nm, for example.

[0009] (First embodiment) An optical inspection device (three-dimensional identification device) 10 according to this embodiment will be described below with reference to FIGS.

[0010] The optical inspection device 10 shown in FIG. 1 includes an image capture device 12 having at least two pixels capable of acquiring depth information of an object B, and a processing device 14 that processes the image captured by the image capture device 12.

[0011] In this embodiment, in order to obtain depth information of the object B, a ToF (Time of Flight) camera capable of obtaining the flight distance of light is used as the imaging device 12.

[0012] The ToF camera 12 includes an imaging optical element 22 and an image sensor (image pickup element) 24. The ToF camera 12 also preferably includes a light source (electromagnetic wave source) 26 that emits light (for example, pulsed light) toward the object B.

[0013] The imaging optics 22 can image an object point P of an object B onto an image point I on an image sensor 24 .

[0014] The image sensor 24 has at least two pixels, and each pixel can convert the intensity of an electromagnetic wave into an electrical signal. Furthermore, the pixel can acquire the arrival time of the electromagnetic wave emitted from the light source 26. In other words, if the time the electromagnetic wave is emitted from the light source 26 is known, the processing device 14 can calculate the time of flight from the time the electromagnetic wave is emitted from the light source 26 to the time the electromagnetic wave emitted from the light source 26 arrives at the image sensor 24. The ToF camera 12 can acquire the time of emission of the electromagnetic wave from the light source 26 and the time the electromagnetic wave arrives at the image sensor 24. In other words, the ToF camera 12 can acquire the time of flight of the electromagnetic wave. At this time, the image sensor 24 of the ToF camera 12 can acquire depth data for the ToF camera 12 as point cloud data for each pixel. Because the ToF camera 12 acquires depth information from the time of flight of light, measurement accuracy is not degraded by the color (wavelength spectrum information) or reflectance of an object.

[0015] Note that the light source 26 that irradiates the object B with light may be provided separately from the ToF camera 12. The ToF camera 12 may, for example, detect the time difference between the time when the light source 26 starts irradiating light and the time when the image sensor 24 acquires light from the object B, and output the travel distance of the light from the object B.

[0016] Any light may be used to irradiate object B in pulses, such as infrared light or visible light. The light to be irradiated may be selected depending on the medium between the ToF camera 12 and object B. For example, the medium may be air, water, glass, or a vacuum. Basically, light that easily passes through the medium is selected. In this embodiment, the electromagnetic wave is assumed to be visible light.

[0017] In this embodiment, an image of an object B is captured by the image sensor 24 of the ToF camera 12. The image sensor 24 is assumed to be, for example, an area sensor. However, this is not a limitation, and the image sensor 24 may be any sensor having two or more pixels. The area sensor has pixels arranged two-dimensionally, and this arrangement is represented as (u, v).

[0018] The time of flight of light is assumed to be t. If the speed of light in a vacuum is c, then the distance traveled by light can be expressed as ct. Here, the speed of light c is constant. However, when light passes through a transparent refractive index medium, the speed of light is the speed c divided by the refractive index. The distance traveled by light can be obtained at each pixel of the image sensor 24. Therefore, the distance traveled can be expressed as a function with (u, v) as arguments, such as ct(u, v).

[0019] The refractive index of air is approximately equal to the refractive index of light in a vacuum. In other words, the refractive index of air is set to 1. Therefore, the optical inspection device 10 according to this embodiment can be used in the same way as in a vacuum, even when air is used as the medium. On the other hand, when water is used as the medium, the refractive index of light is, for example, 1.33.

[0020] Here, an XYZ Cartesian coordinate space is taken as shown in Fig. 1. For example, the axis perpendicular to the image sensor 24 is set as the Z axis. Fig. 1 shows the ToF camera 12 along the ZX plane.

[0021] When an object B is captured by the ToF camera 12 in the XYZ Cartesian coordinate space, three-dimensional point cloud data of the surface (object surface) S of the object B can be obtained. The Z coordinate is particularly called the depth. The point where an object point P on the surface S of the object B is imaged on a pixel of the ToF camera 12 is called an image point I. The three-dimensional point cloud data is composed of only the object points P whose image points I are located on each pixel of the image sensor 24. In other words, any two points in the point cloud data will have various distances from each other. In other words, the point cloud data is composed of discrete points in the XYZ Cartesian coordinate space. Such point cloud data is information about points on the surface S of the object B, but does not contain surface information. Surface information is, for example, the normal direction. The normal direction cannot be immediately obtained from the position information of the points alone.

[0022] CAD data is numerical data that can describe the three dimensions of object B in detail. However, CAD data cannot be immediately created from point cloud data. To create CAD data, it is necessary to estimate the surfaces of adjacent point clouds using the least squares method, as in Non-Patent Document 1. On the other hand, if the normal direction, which is surface information, is known, CAD data can be immediately created. For example, the STL (Stereolithography) format, which is one format of CAD data, can be composed of point cloud data and normal directions.

[0023] The processing device 14 is configured, for example, by a computer or the like, and includes a processor (processing circuit) 14a and a storage medium 14b. The processor 14a includes any of a CPU (Central Processing Unit), an ASIC (Application Specific Integrated Circuit), a microcomputer, an FPGA (Field Programmable Gate Array), and a DSP (Digital Signal Processor). The storage medium 14b may include a non-transitory auxiliary storage device in addition to a main storage device such as a memory. Examples of the storage medium 14b include a hard disk drive (HDD), a solid state drive (SSD), a magnetic disk, an optical disk (CD-ROM, CD-R, DVD, etc.), a magneto-optical disk (MO, etc.), and a non-volatile memory such as a semiconductor memory that can be written to and read from at any time.

[0024] The processing device 14 may be provided with only one processor 14a and one storage medium 14b, or may be provided with multiple processors 14a and multiple storage mediums 14b. In the processing device 14, the processor 14a performs processing by executing programs stored in the storage medium 14b or the like. The programs executed by the processor 14a of the processing device 14 may be stored in a computer (server) connected to the processing device 14 via a network such as the Internet, or in a server in a cloud environment. In this case, the processor 14a downloads the programs via the network. Here, it is assumed that the processing device 14 is equipped with a three-dimensional identification processing program (optical inspection program).

[0025] In the processing device 14, various calculation processes based on the image acquired by the image sensor 24 are executed by the processor 14a and the like, and the storage medium 14b functions as a data storage unit.

[0026] Furthermore, at least a part of the processing by the processing device 14 may be executed by a cloud server configured in a cloud environment. The infrastructure of the cloud environment is configured by a virtual processor such as a virtual CPU and a cloud memory. In one example, image acquisition by the image sensor 24 and various calculation processes based on the images acquired by the image sensor 24 are executed by the virtual processor, and the cloud memory functions as a data storage unit.

[0027] With the above configuration, the operation of the optical inspection device 10 according to this embodiment will be described with reference to Fig. 2. Fig. 2 shows a series of steps in which the processing device 14 outputs a normal vector N as normal information of the surface S of the object B.

[0028] The processing device 14 controls the image sensor 24 and the light source 26 to irradiate pulsed light from the light source 26 onto the object B, and acquires the light reflected from the surface S of the object B with the image sensor 24. To this end, the processing device 14 acquires point cloud data of the surface S of the object B (step S1).

[0029] The coordinates of a point on the surface S of object B are written as (X, Y, Z). In this case, the depth Z can be expressed as Z = Z(X, Y) using X and Y as arguments. Then, by using differential geometry, the unit normal vector N of the surface S of object B can be written as the following equation (1).

[0030]

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[0031]

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[0032] A discretized finite difference method (hereinafter sometimes referred to as the discretized finite difference method) is applied as a partial derivative of the two-dimensional coordinates (u, v) of the depth Z that can be acquired by each pixel of the image sensor 24 of the ToF camera 12 (step S2). In this method, instead of differentiation that handles continuous values, the change in depth Z of the surface S of the object B in the image is quantified by calculating the difference in depth between adjacent pixels separated by a finite distance that can be considered sufficiently small. This makes it possible to numerically obtain the value of the normal vector N in equation (2). Therefore, the processing device 14 can perform a depth change calculation process on point cloud data (images) containing depth information of the object B, calculating the change in depth relative to a change in position between each pixel on the image. Surface information can be obtained from this change in depth. In particular, the normal vector N can be obtained from the change in depth relative to a change in position.

[0033] Then, the processing device 14 can acquire and output the normal vector N, which is information about the surface S of the object B. Once the normal vector N has been acquired, it can be immediately converted into CAD data (e.g., STL data) that describes the three-dimensional information in detail, and the CAD data can be output (step S3). Therefore, the processing device 14 can output the normal vector N based on the data of a large number of adjacent points.

[0034] Identifying the individual object B is important in various fields. If object B is an independent solid, the surface S of object B forms a closed space. The processing device 14 cannot determine whether object B is a closed space from the point cloud data acquired by the image sensor 24. On the other hand, the processing device 14 can determine whether object B is a closed space if it is given information about the surface S. That is, according to this embodiment, the processing device 14 has the advantage of being able to determine (identify) whether object B is a closed space by acquiring the normal vector N. Furthermore, information about the inclination of the surface S of object B is important in various fields. The inclination of the surface S can be accurately determined from the normal vector N. For example, when cutting object B, the cutting machine needs to determine the inclination of the surface S of object B. Or, when object B is grasped by a robot hand, the robot needs to determine the inclination or orientation of the surface S of object B. Or, during product quality inspection, the robot needs to determine whether the inclination of the surface S of object B is within an acceptable range. According to this embodiment, the processing device 14 can quickly determine the normal vector N based on the point cloud data acquired by the image sensor 24. Therefore, the processing device 14 according to this embodiment can obtain (calculate) the three-dimensional shape of the surface S of the object B based on the point cloud data including information about the flight distance of light from the object B. Therefore, this embodiment is an effective means for realizing a three-dimensional identification processing device 14, optical inspection device 10, three-dimensional identification processing method, and three-dimensional identification processing program that can acquire normal information of the surface S of the object B and depth information of the object B using an imaging device 12 that can acquire, for example, the flight distance of light.

[0035] Therefore, according to this embodiment, it is possible to provide a three-dimensional identification processing device 14, an optical inspection device 10, a three-dimensional identification processing method, and a three-dimensional identification processing program that can acquire normal information of an object surface S using an imaging device 12 that can acquire depth information of an object B.

[0036] (Second embodiment) An optical inspection device 10 according to a second embodiment will be described with reference to Fig. 3. This embodiment is a modified example of the optical inspection device 10 according to the first embodiment, and the same members as those described in the first embodiment or the same reference numerals are used, and detailed description thereof will be omitted.

[0037] As shown in FIG. 3, in the optical inspection device 10 according to this embodiment, the lens 22 of the ToF camera 12 has a focal length f, and forms an image of an object point P that is sufficiently far from the lens 22 onto an image point I on the image sensor 24.

[0038] A global Cartesian coordinate system (X, Y, Z) is set in the entire space including the imaging device 12 of the optical inspection device 10 according to this embodiment, with the Z axis coinciding with the optical axis of the imaging lens 22. The imaging lens 22 can be discussed as a virtual pinhole placed at the origin O of the global coordinate system. In other words, in the following discussion, the lens 22 is considered to perform the same function as a pinhole camera. Here, the principal plane of the lens 22 is the plane where Z = 0. The local coordinate system (u, v) on the image sensor 24 is spaced from the origin O of the global coordinate system by a focal length f along the Z axis of the global coordinate system. The origin O' of the local coordinate system is on the optical axis. The normal vector N of the surface S of the object B is defined as a unit vector perpendicular to the surface S of the object B.

[0039] For each pixel (u, v) in the image, the ToF camera 12 acquires the distance from the origin O of the global coordinate system to an object point (X, Y, Z) on the surface S of the object B. This distance can be written as ct, where c is the speed of light and t is the time of flight of light.

[0040] X, Y, and Z can be written as the following equation (3).

[0041]

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[0042] Here, based on differential geometry, the partial derivative of depth Z with respect to X or Y can be written as the following equation (4).

[0043]

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[0044]

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[0045]

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[0046] By substituting equation (5) into equation (1), it can be seen that the normal vector N at object point P on surface S of object B can be expressed as (∂uZ, ∂vZ), which is the partial derivative (∂uZ, ∂vZ) of the depth Z that can be obtained at each pixel, with respect to the two-dimensional coordinates (u, v) of the image sensor 24. In other words, the normal vector N can be obtained from the change in depth with respect to two orthogonal position changes on the image. In other words, each component of the normal vector N can be obtained from each of the change in depth with respect to two orthogonal position changes on the image. However, the direction of the normal vector N can also be limited from only the change in depth with respect to a position change in one direction. In other words, the information on the normal vector N can be obtained from the change in depth with respect to a position change in at least one direction.

[0047] The discrete difference method is applied as a partial derivative of the two-dimensional coordinates (u, v) of the depth Z, which can be acquired by each pixel of the image sensor 24 of the ToF camera 12. Instead of differentiation that handles continuous values, this method quantifies the change in depth Z in the image by calculating the difference in depth between adjacent pixels separated by a finite distance that can be considered sufficiently small. This allows the value of Equation (5) to be numerically obtained. The inverse matrix of the Jacobian matrix can also be obtained using the discrete difference method (step S2 in FIG. 2). Substituting Equation (5) obtained in this way into Equation (1) allows the value of the normal vector N to be numerically obtained. In other words, the processing device 14 according to this embodiment can perform depth change calculation processing on point cloud data (images) containing depth information of object B, to calculate the change in depth relative to the change in position between each pixel in the image, which is an important parameter for surface information.

[0048] The processing device 14 can then acquire and output a normal vector N, which is information about the surface S of the object B. Once the normal vector N has been acquired, it can be immediately converted into CAD data (e.g., STL data) that describes the three-dimensional information in detail, and the CAD data can be output (step S3 in FIG. 2). Therefore, the processing device 14 can output the normal vector N based on the cloud data of multiple adjacent points acquired by the image sensor 24 (step S1 in FIG. 2).

[0049] As described above, individual identification of object B is important in various fields. Like the processing device 14 according to the first embodiment, the processing device 14 according to this embodiment can quickly determine the normal vector N based on point cloud data acquired by the image sensor 24. Therefore, the processing device 14 according to this embodiment can obtain (calculate) the three-dimensional shape of the surface S of object B based on point cloud data that includes information about the flight distance of light from object B. Therefore, this embodiment is an effective means for realizing a three-dimensional identification processing device 14, optical inspection device 10, three-dimensional identification processing method, and three-dimensional identification processing program that can acquire normal information of the surface S of object B and depth information of object B using, for example, an imaging device 12 that can acquire the flight distance of light.

[0050] According to this embodiment, it is possible to provide a three-dimensional identification processing device 14, an optical inspection device 10, a three-dimensional identification processing method, and a three-dimensional identification processing program that can acquire normal information of an object surface S using an imaging device 12 that can acquire depth information of an object B.

[0051] FIG. 4 shows an application example in which the image sensor 24 and the light source 26 are controlled by the processing device 14 according to this embodiment to cause the image sensor 24 to acquire an image, and the image acquired by the image sensor 24 is processed.

[0052] As shown in Fig. 4, the processing device 14 according to this embodiment can process the point cloud data on the left side of Fig. 4 as data having a normal vector N on the right side of Fig. 4. As shown in the diagram on the right side of Fig. 4, the shape of the fish can be grasped more accurately than when only point cloud data is acquired.

[0053] (Third embodiment) An optical inspection device 10 according to the third embodiment will be described with reference to Fig. 5. This embodiment is basically the same as the second embodiment. The differences will be described below.

[0054] FIG. 5 shows the image sensor 24 of the imaging device 12 of the optical inspection apparatus 10 according to the third embodiment. The image sensor 24 is composed of a large number of pixels arranged on a regular lattice. The size of each pixel is Δu×Δv. The size of the image sensor 24 is Lu×Lv.

[0055] From equation (5), it can be seen that the partial derivative of depth Z with respect to X or Y can be described using the partial derivatives of depth Z with respect to u and v. In other words, the normal vector N at object point P on surface S of object B can be expressed as (∂uZ, ∂vZ) obtained by partially differentiating depth Z, which can be acquired at each pixel, with respect to the two-dimensional coordinates (u, v) of the image sensor 24. Here, if depth Z is a function with (u, v) as arguments, then because (u, v) are on a regular lattice, a Fourier series can be expanded as shown in the following equation (7).

[0056]

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[0057] Next, for example, let's try partially differentiating equation (7) with respect to u. Then,

[0058]

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[0059]

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[0060] By substituting the Jacobian matrix expressed by Equation (8), Equation (9), and Equation (6) into Equation (1), the normal vector N of the surface S of object B can be calculated. The calculation of this normal vector N can be processed quickly because (u, v) are on a regular lattice. In other words, the processing device 14 can quickly acquire the normal vector N, which is information about the surface S of object B. Furthermore, Equation (7) is a continuous function. That is, it is not a discrete function. Therefore, the depth for any continuous point can be calculated from the point cloud data, which was discrete when captured. Furthermore, Equation (8) and Equation (9) are continuous functions. That is, they are not discrete functions. Therefore, the normal vector calculated by substituting the Jacobian matrix expressed by Equation (8), Equation (9), and Equation (6) into Equation (1) is a continuous function. That is, the normal vector for any continuous point can be calculated from the point cloud data, which was discrete when captured.

[0061] The processing device 14 can then acquire and output a normal vector N, which is information about the surface S of the object B. Once the normal vector N has been acquired, the image captured by the image sensor 24 can be immediately converted into CAD data (e.g., STL data) that describes the three-dimensional information in detail, and the CAD data can be output. Therefore, the processing device 14 can output the normal vector N based on a large number of adjacent point cloud data. In other words, the value of the normal vector N can be obtained numerically. Therefore, the processing device 14 can perform depth change calculation processing on the point cloud data (image) containing depth information of the object B, calculating the change in depth relative to the change in position between each pixel on the image, which is an important parameter for surface information.

[0062] As described above, individual identification of object B is important in various fields. Like the processing device 14 according to the second embodiment, the processing device 14 according to this embodiment can quickly determine the normal vector N based on point cloud data acquired by the image sensor 24. Therefore, the processing device 14 according to this embodiment can obtain (calculate) the three-dimensional shape of the surface S of object B based on point cloud data that includes information about the flight distance of light from object B. Therefore, this embodiment is an effective means for realizing a three-dimensional identification processing device 14, optical inspection device 10, three-dimensional identification processing method, and three-dimensional identification processing program that can acquire normal information of the surface S of object B and depth information of object B using, for example, an imaging device 12 that can acquire the flight distance of light.

[0063] In particular, when gripping object B with a robot hand, processing device 14 needs to quickly grasp the inclination of object surface S. In this embodiment, the local coordinate system (u, v) set on image sensor 24 is on a regular lattice, so processing device 14 can acquire normal information of object surface S through high-speed processing. Alternatively, high speed is also required in the inspection of product surfaces (corresponding to object surface S) flowing on a production line, and in this embodiment, the local coordinate system (u, v) set on image sensor 24 is on a regular lattice, so processing device 14 can acquire normal information of object surface S through high-speed processing.

[0064] In the above-described first to third embodiments, examples have been described in which a ToF camera is used as the imaging device 12. If a camera other than a ToF camera, which has at least two pixels that can acquire the flight distance of light from an object, is used as the imaging device 12, it is not necessary to use a ToF camera.

[0065] According to at least one of the above-described embodiments of the three-dimensional identification processing device 14, optical inspection device 10, three-dimensional identification processing method, and three-dimensional identification processing program, normal information of the surface S of the object B can be obtained using an imaging device 12 that can acquire depth information of the object B.

[0066] Although the embodiments of the present invention have been described above, these embodiments are presented as examples and are not intended to limit the scope of the invention. These novel embodiments may be embodied in various other forms, and various omissions, substitutions, and modifications may be made without departing from the spirit of the invention. These embodiments are included within the scope and spirit of the invention, and are also included in the inventions and their equivalents as defined in the claims. [Explanation of symbols]

[0067] 10...optical inspection device, 12...ToF camera (imaging device), 14...processing device (three-dimensional identification processing device), 14a...processor, 14b...storage medium, 22...imaging optical element, 24...image sensor, 26...light source

Claims

1. performing a depth change calculation process for calculating a change in depth in response to a position change on an image including depth information, the image being acquired using an imaging device having at least two pixels and capable of acquiring depth information of an object; Obtaining normal information of the surface of the object; A three-dimensional identification processing device having a processor.

2. The normal information is a normal vector. The three-dimensional identification processing device according to claim 1 .

3. the processor identifying a closed volume from the normal vector. The three-dimensional identification processing device according to claim 2 .

4. the processor identifies the tilt of the object surface of the object from the normal vector. The three-dimensional identification processing device according to claim 2 .

5. the processor uses an inverse matrix of a Jacobian matrix for global space coordinates, with local coordinates on the image as arguments, in the depth change calculation process; The three-dimensional identification processing device according to claim 2 .

6. The processor: The depth is expressed as a function of local coordinates on the image, and a Fourier series is obtained by performing a Fourier transform on the function of local coordinates, performing the depth change calculation process using the Fourier series; The three-dimensional identification processing device according to any one of claims 1 to 5.

7. Assume that each pixel of the image is arranged on a regular lattice, The processor processes the Fourier transform using a fast Fourier transform algorithm. The three-dimensional identification processing device according to claim 6 .

8. the imaging device having at least two pixels capable of acquiring depth information of the object; The processing device according to claim 1 ; An optical inspection device comprising:

9. The imaging device acquires the flight distance of light from the object. The optical inspection device according to claim 8 .

10. performing a depth change calculation process for calculating a change in depth in response to a position change on an image including depth information, the image being acquired using an imaging device having at least two pixels capable of acquiring depth information of an object; obtaining normal information of the surface of the object; A three-dimensional identification processing method, comprising:

11. causing the imaging device to acquire the flight distance of light from the object; The three-dimensional identification processing method according to claim 10, comprising:

12. performing a depth change calculation process for calculating a change in depth in response to a position change on an image including depth information, the image being acquired using an imaging device having at least two pixels capable of acquiring depth information of an object; obtaining normal information of the surface of the object; A three-dimensional identification processing program that causes a computer to execute the above.

13. Controlling the imaging device to acquire a flight distance of light from the object; The three-dimensional identification processing program according to claim 12, which causes the computer to execute the following.

Images