Painting evaluation apparatus and painting evaluation method

Abstract

Provided are a coating evaluation device and a coating evaluation method, configured so that input light having a first intensity distribution is projected onto a coated surface, and a second intensity distribution of light reflected from the coated surface is acquired. Further, on the basis of the curved shape of the coated surface, the first intensity distribution which is associated with a reference intensity distribution is set, and an evaluation value corresponding to a second intensity distribution is estimated using an evaluation model by which an evaluation value of image clarity of the coated surface is output with respect to input including the second intensity distribution.

Description

Technical Field

[0001] The present invention relates to a coating evaluation apparatus and a coating evaluation method.

Background Art

[0002] There is known an invention in which shading pattern light, which is a repeating pattern of light and shade, is projected onto a coated surface, the shading pattern formed on the coated surface is photographed to calculate the luminance distribution in the repeating direction of light and shade, the luminance distribution is Fourier-transformed, a wavelength distribution, which is a distribution of amplitudes with respect to wavelength, is calculated, an integrated value of an interval corresponding to a predetermined wavelength range of the wavelength distribution is calculated, and the coated surface is evaluated based on the integrated value (Patent Document 1).

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] According to the invention described in Patent Document 1, when the coated surface is a curved surface, the boundary between light and dark in the formed shading pattern may become blurred, and there is a problem that the accuracy in evaluating the distinctness of image of the coated surface may decrease.

[0005] The present invention has been made in view of the above problems, and an object thereof is to provide a coating evaluation apparatus and a coating evaluation method capable of accurately evaluating the distinctness of image of a coated surface even when the coated surface is a curved surface.

Means for Solving the Problems

[0006] The coating evaluation apparatus and coating evaluation method according to one aspect of the present invention irradiate a coating surface with incident light having a first intensity distribution, and acquire a second intensity distribution of the reflected light from the coating surface. Further, based on the curved shape of the coating surface, the first intensity distribution associated with the reference intensity distribution is set, and an evaluation value of the distinctness of image of the coating surface is output for an input including the second intensity distribution using an evaluation model, and an evaluation value corresponding to the second intensity distribution is estimated.

Advantages of the Invention

[0007] According to the present invention, even when the coating surface is a curved surface, the distinctness of image of the coating surface can be accurately evaluated.

Brief Description of the Drawings

[0008]

Figure 1

Figure 2

Figure 3A

Figure 3B

Figure 4A

Figure 4B

Embodiments for Carrying Out the Invention

[0009] Hereinafter, embodiments of the present invention will be described with reference to the drawings. In the description of the drawings, the same parts are denoted by the same reference numerals and the description thereof is omitted.

[0010] [Configuration of Coating Evaluation Apparatus] Referring to FIG. 1, a configuration example of a painting evaluation apparatus according to this embodiment will be described. As shown in FIG. 1, the painting evaluation apparatus includes a shape acquisition unit 11, a strength acquisition unit 15, a light source unit 19, and a controller 100. In addition, the painting evaluation apparatus may include a material acquisition unit 13 and an output unit 400. The shape acquisition unit 11, the material acquisition unit 13, the strength acquisition unit 15, the light source unit 19, and the output unit 400 are connected to the controller 100.

[0011] The shape acquisition unit 11 acquires shape information representing the curved shape of the painted surface to be evaluated for painting. More specifically, the shape acquisition unit 11 may acquire the design data of the painted surface as the shape information. For example, CAD (Computer-aided design) data can be cited as the design data. The design data is not limited to this as long as it represents the degree (curvature) of the curvature of the painted surface.

[0012] The shape acquisition unit 11 may acquire the stored design data of the painted surface from a database (not shown), or may acquire the design data of the painted surface from an external connection device (not shown) via a wired or wireless network. In addition, the shape acquisition unit 11 may acquire the design data based on the user's input.

[0013] In addition, the shape acquisition unit 11 may acquire the measurement data obtained by measuring the painted surface as the shape information. For example, a 3D scanner can be cited as the shape acquisition unit 11.

[0014] In addition, the shape acquisition unit 11 may acquire the position information of the region of the painted surface irradiated with the incident light by the light source unit 19 described later, and acquire the shape information based on the position information. That is, based on the position of the region irradiated with the incident light, the design data of the painted surface or the measurement data obtained by measuring the painted surface may be acquired.

[0015] The material acquisition unit 13 acquires the material information of the painted surface. More specifically, the material acquisition unit 13 acquires information such as the type and color of the member as the material information of the painted surface. The material acquisition unit 13 may acquire the stored material information of the painted surface from a database (not shown), or may acquire the material information of the painted surface from an external connection device (not shown) via a wired or wireless network. Alternatively, the material acquisition unit 13 may acquire the material information based on the input of the user.

[0016] The light source unit 19 irradiates the painted surface with incident light having the set first intensity distribution. More specifically, the light source unit 19 has a plurality of light sources arranged in a plane, and the intensity of the light emitted by each light source is adjusted by a controller 100 (described later), so as to irradiate the painted surface with incident light having the first intensity distribution.

[0017] Examples of the light source constituting the light source unit 19 include various ones such as an LED lamp, an incandescent bulb, and a fluorescent lamp.

[0018] Here, the first intensity distribution of the incident light is calculated by correcting the reference intensity distribution based on the shape information, as will be described later. The reference intensity distribution may have a periodic structure in the first direction. FIG. 3A shows an example of a reference intensity distribution having a striped periodic structure in which bright portions and dark portions are continuously arranged along the R1 direction. Further, the reference intensity distribution may have a periodic structure in a second direction different from the first direction. FIG. 3B shows an example of a reference intensity distribution having a striped periodic structure in which bright portions and dark portions are continuously arranged along the R2 direction different from the R1 direction.

[0019] Note that the reference intensity distribution may have a periodic structure in the direction of the main direction vector at a predetermined position on the painted surface. Here, the main direction vector means a tangent vector when the curvature of a curve appearing in the common part of the plane including the tangent vector and the normal vector of the painted surface and the painted surface becomes the main curvature of the painted surface at a predetermined position.

[0020] For example, based on the shape information acquired by the material acquisition unit 13, the controller 100 described later may calculate the main direction vector at a predetermined position on the painted surface, and use the intensity distribution having a periodic structure in the direction of the main direction vector as the reference intensity distribution. Further, the painted surface may be rotated with respect to the light source unit 19 about an axis parallel to the incident direction of the incident light so that the first direction in which the reference intensity distribution has a periodic structure and the main direction vector at a predetermined position on the painted surface are in a coincident positional relationship.

[0021] In addition, at the boundary between the "bright part" and the "dark part" of the reference intensity distribution, the intensity of the light may change intermittently. Therefore, for example, when the intensity of the incident light in the "bright part" (first region) is equal to or greater than the first threshold value, the intensity of the incident light in the "dark part" (second region) may be equal to or less than a second threshold value smaller than the first threshold value.

[0022] In addition, for alignment, the light source unit 19 may irradiate incident light including the first marking pattern. FIG. 4A shows an example of a rectangular first marking pattern set so as to surround the incident light of the first intensity distribution.

[0023] Note that since the painted surface can have various curved shapes, the intensity distribution of the reflected light from the painted surface can change according to the curved shape. FIG. 4B shows a second marking pattern that is curved from a rectangular shape according to the curved shape. By comparing the first marking pattern and the second marking pattern, the degree of curvature of the region irradiated with the first marking pattern can be calculated.

[0024] The intensity acquisition unit 15 acquires a second intensity distribution of the reflected light from the painted surface that has received the incident light. More specifically, the intensity acquisition unit 15 is a digital camera equipped with a solid-state imaging device such as a CCD or a CMOS, and images a region of the painted surface irradiated with the incident light to acquire a digital image. The second intensity distribution is represented by the intensity of the light at each pixel constituting the digital image.

[0025] When the focal length, the angle of view of the lens, the vertical and horizontal angles of the camera, etc. are set, the intensity acquisition unit 15 images the area irradiated with the incident light.

[0026] Further, the intensity acquisition unit 15 may acquire the intensity distribution of the reflected light from the area irradiated with the first marking pattern as the second marking pattern among the painted surfaces.

[0027] Note that by comparing the first marking pattern and the second marking pattern, the degree of curvature of the area irradiated with the first marking pattern can be calculated. For example, the controller 100 described later may acquire the reference shape information of the area irradiated with the first marking pattern based on the difference between the first marking pattern and the second marking pattern.

[0028] Then, the position information of the area on the painted surface having the shape information that matches the reference shape information may be acquired. Thereby, alignment can be performed in accordance with the area irradiated with the incident light, and the shape information can be acquired.

[0029] The controller 100 is a general-purpose computer including a CPU (Central Processing Unit), a memory, a storage device, an input / output unit, and the like.

[0030] A computer program (painting evaluation program) for causing the controller 100 to function as a painting evaluation device is installed in the controller 100. By executing the computer program, the controller 100 functions as a plurality of information processing circuits included in the painting evaluation device.

[0031] Here, an example of realizing a plurality of information processing circuits included in the painting evaluation device by software is shown. Of course, it is also possible to prepare dedicated hardware for executing each of the following information processes to configure the information processing circuit. Further, the plurality of information processing circuits may be configured by individual hardware.

[0032] The controller 100 includes an intensity correction unit 110, an evaluation model setting unit 120, and an evaluation value estimation unit 130.

[0033] The intensity correction unit 110 calculates a first intensity distribution associated with a reference intensity distribution based on the shape information. The calculated first intensity distribution is set as the intensity distribution of the incident light irradiated by the light source unit 19. Specifically regarding the calculation of the first intensity distribution, the second intensity distribution includes a light scattering component generated by the deviation of the curved shape of the painted surface from the planar shape. Therefore, the intensity correction unit 110 calculates the first intensity distribution so as to cancel out the light scattering component on the painted surface due to the deviation included in the second intensity distribution.

[0034] The light scattering component generated by the deviation of the curved shape of the painted surface from the planar shape is calculated by using simulation techniques such as rendering techniques using computer graphics and shading. By using these simulation techniques, the intensity correction unit 110 can calculate the first intensity distribution so as to cancel out the light scattering component on the painted surface due to the deviation included in the second intensity distribution.

[0035] For example, the smaller the deviation of the curved shape from the planar shape, the smaller the difference between the reference intensity distribution and the first intensity distribution. Also, when the curved shape is a planar shape, the reference intensity distribution and the first intensity distribution coincide.

[0036] The evaluation model setting unit 120 sets an evaluation model that outputs an evaluation value of the distinctness of image of the painted surface for an input including the second intensity distribution. Here, the evaluation model is a learning model generated by machine learning based on teacher data that is a set of the intensity distribution of the reflected light obtained by irradiating a reference light having a reference intensity distribution on an evaluated painted surface having a planar shape and the evaluation value of the distinctness of image of the evaluated painted surface.

[0037] Here, the evaluation value of distinctness of image is an index determined by, for example, at least one of the smoothness of the painted surface, the ratio of diffuse reflection in the reflected light on the painted surface, and the resolution of the image reflected on the painted surface. The evaluation value of the distinctness of image of the evaluated painted surface is a numerical value given in advance to the evaluated painted surface by other distinctness-of-image evaluation methods.

[0038] Note that the evaluation model setting unit 120 may set an evaluation model that outputs an evaluation value of the distinctness of image for an input including material information and the second strength distribution. In this case, the evaluation model is a learning model generated by machine learning based on teacher data that is a set of the material information of the evaluated painted surface having a planar shape, the intensity distribution of the reflected light obtained by irradiating the evaluated painted surface with reference light having a reference strength distribution, and the evaluation value of the distinctness of image of the evaluated painted surface.

[0039] Examples of the method for generating a learning model by machine learning include a method using one or a combination of two or more of neural network, support vector machine, Random Forest, XGBoost, LightGBM, PLS regression, Ridge regression, and Lasso regression. The method for generating a learning model by machine learning is not limited to the examples given here.

[0040] The evaluation model setting unit 120 may perform machine learning based on teacher data acquired from a database (not shown) to set an evaluation model. Further, the evaluation model may be stored in advance in a database (not shown), and the evaluation model setting unit 120 may set the evaluation model acquired from the database.

[0041] The evaluation value estimation unit 130 estimates an evaluation value corresponding to the second intensity distribution using the set evaluation model. More specifically, when the set evaluation model is an evaluation model that outputs an evaluation value of the distinctness of image of the painted surface for an input including the second intensity distribution, the evaluation value estimation unit 130 inputs the second intensity distribution to the evaluation model. Then, the evaluation value estimation unit 130 uses the value output from the evaluation model as the evaluation value corresponding to the second intensity distribution. The evaluation value corresponding to the second intensity distribution is an estimated evaluation value regarding the distinctness of image of the painted surface.

[0042] When the set evaluation model is an evaluation model that outputs an evaluation value of the distinctness of image of the painted surface for an input including material information and the second intensity distribution, the evaluation value estimation unit 130 inputs the material information and the second intensity distribution to the evaluation model. Then, the evaluation value estimation unit 130 uses the value output from the evaluation model as the evaluation value corresponding to the combination of the material information and the second intensity distribution. The evaluation value corresponding to the second intensity distribution is an estimated evaluation value regarding the distinctness of image of the painted surface.

[0043] The output unit 400 outputs the estimated evaluation value regarding the distinctness of image of the painted surface.

[0044] [Processing Procedure of Painting Evaluation Apparatus] Next, the processing procedure of the painting evaluation apparatus according to the present embodiment will be described with reference to the flowchart of FIG. 2. It is assumed that the evaluation model has already been set by the evaluation model setting unit 120 before the processing shown in the flowchart of FIG. 2 is started.

[0045] In step S101, the shape acquisition unit 11 acquires shape information. Also, the material acquisition unit 13 acquires material information.

[0046] In step S103, the intensity correction unit 110 calculates a first intensity distribution associated with the reference intensity distribution based on the shape information.

[0047] In step S105, the controller 100 sets the first intensity distribution of the incident light.

[0048] In step S107, the intensity acquisition unit 15 acquires the second intensity distribution of the reflected light.

[0049] In step S111, the evaluation value estimation unit 130 estimates an evaluation value corresponding to the second intensity distribution using the set evaluation model.

[0050] In step S113, the output unit 400 outputs the evaluation value estimated by the evaluation value estimation unit 130.

[0051] [Effect of Embodiment] As described in detail above, the painting evaluation apparatus, painting evaluation method, and painting evaluation program according to the present embodiment irradiate the painted surface with incident light having a first intensity distribution, and acquire the second intensity distribution of the reflected light from the painted surface. Further, based on the curved shape of the painted surface, the first intensity distribution associated with the reference intensity distribution is set, and an evaluation value corresponding to the second intensity distribution is estimated using an evaluation model that outputs an evaluation value of the distinctness of image of the painted surface for an input including the second intensity distribution.

[0052] Thereby, even when the painted surface is a curved surface, the distinctness of image of the painted surface can be accurately evaluated. Further, since the evaluation value is estimated by the evaluation model, dedicated equipment or skilled personnel for evaluating the distinctness of image of the painted surface are not required, and the cost for evaluating the distinctness of image of the painted surface can be reduced.

[0053] Further, in the painting evaluation apparatus, painting evaluation method, and painting evaluation program according to the present embodiment, the smaller the deviation of the curved shape from the planar shape, the smaller the difference between the reference intensity distribution and the first intensity distribution may be. Further, when the curved shape is a planar shape, the reference intensity distribution and the first intensity distribution may coincide. Thereby, the light scattering component on the painted surface due to the deviation of the curved shape from the planar shape can be removed from the second intensity distribution. That is, the first intensity distribution is calculated so as to cancel the light scattering component on the painted surface due to the deviation included in the second intensity distribution.

[0054] Furthermore, in the painting evaluation apparatus, painting evaluation method, and painting evaluation program according to the present embodiment, the evaluation model may be a learning model generated by machine learning based on teacher data that combines the intensity distribution of reflected light obtained by irradiating a reference light having a reference intensity distribution on an evaluated painted surface having a planar shape and the evaluation value of the distinctness of image of the evaluated painted surface. Thereby, the distinctness of image of a painted surface that is a curved surface can be accurately evaluated by the evaluation model generated from the teacher data regarding the evaluated painted surface having a planar shape.

[0055] Also, in the painting evaluation apparatus, painting evaluation method, and painting evaluation program according to the present embodiment, the evaluation value of the distinctness of image of the painted surface may be an index determined by at least one of the smoothness of the painted surface, the ratio of diffuse reflection in the reflected light on the painted surface, and the resolution of the image reflected on the painted surface. In this way, the criteria for evaluating the distinctness of image of the painted surface are clarified.

[0056] Furthermore, the painting evaluation apparatus, painting evaluation method, and painting evaluation program according to the present embodiment may acquire the design data of the painted surface as shape information, or may acquire the measurement data obtained by measuring the painted surface as shape information. Thereby, the influence due to the curved shape of the painted surface can be suppressed, and the distinctness of image of the painted surface can be accurately evaluated.

[0057] Also, in the painting evaluation apparatus, painting evaluation method, and painting evaluation program according to the present embodiment, the reference intensity distribution may have a periodic structure in the first direction. For example, the reference intensity distribution may have a stripe-like periodic structure. Thereby, the painted surface can be evaluated based on the luminance distribution along the direction having the periodic structure, and the distinctness of image of the painted surface can be accurately evaluated.

[0058] Furthermore, in the painting evaluation apparatus, painting evaluation method, and painting evaluation program according to the present embodiment, the reference intensity distribution may have a periodic structure in a second direction different from the first direction. Thereby, based on the luminance distribution along the direction having the periodic structure, the painted surface can be evaluated, and the distinctness of image of the painted surface can be accurately evaluated. Furthermore, the influence caused by the direction of the periodic structure can be reduced, and the painted surface can be evaluated.

[0059] Also, the painting evaluation apparatus, painting evaluation method, and painting evaluation program according to the present embodiment may calculate a main direction vector at a predetermined position on the painted surface based on the shape information, and set an intensity distribution having a periodic structure in the direction of the main direction vector as the reference intensity distribution. Thereby, the influence of the curved shape of the painted surface can be suppressed, and the distinctness of image of the painted surface can be accurately evaluated.

[0060] Furthermore, in the painting evaluation apparatus, painting evaluation method, and painting evaluation program according to the present embodiment, the reference intensity distribution may include a first region where the intensity of the incident light is equal to or greater than a first threshold value, and a second region where the intensity of the incident light is less than the first threshold value and equal to or less than a second threshold value. Thereby, at the boundary between the "bright part" and the "dark part" of the reference intensity distribution, the intensity of the light changes intermittently, and the distinctness of image of the painted surface can be accurately evaluated.

[0061] Also, the painting evaluation apparatus, painting evaluation method, and painting evaluation program according to the present embodiment may acquire position information of the region on the painted surface irradiated with the incident light, and acquire shape information based on the position information. Thereby, alignment between the painted surface to be evaluated and the curved shape represented by the shape information can be performed.

[0062] Furthermore, the painting evaluation apparatus, painting evaluation method, and painting evaluation program according to the present embodiment may irradiate incident light including a first marking pattern onto a painted surface, and acquire, as a second marking pattern, an intensity distribution of reflected light from a region of the painted surface irradiated with the first marking pattern. Then, based on the difference between the first marking pattern and the second marking pattern, reference shape information of the region irradiated with the first marking pattern may be acquired, and position information of a region on the painted surface having shape information that matches the reference shape information may be acquired. Thereby, alignment can be performed between the painted surface to be evaluated and the curved shape represented by the shape information.

[0063] Also, the painting evaluation apparatus, painting evaluation method, and painting evaluation program according to the present embodiment may acquire material information of the painted surface, and estimate an evaluation value corresponding to a combination of the material information and the second intensity distribution using an evaluation model that outputs an evaluation value of the distinctness of image of the painted surface for an input including the material information and the second intensity distribution. By using, in addition to the intensity distribution of the reflected light, the material information of the painted surface for the evaluation of the distinctness of image of the painted surface, the distinctness of image of the painted surface can be accurately evaluated.

[0064] Furthermore, in the painting evaluation apparatus, painting evaluation method, and painting evaluation program according to the present embodiment, the evaluation model may be a learning model generated by machine learning based on teacher data including, as a set, material information of an evaluated painted surface having a planar shape, an intensity distribution of reflected light obtained by irradiating the evaluated painted surface with reference light having a reference intensity distribution, and an evaluation value of the distinctness of image of the evaluated painted surface. Thereby, the distinctness of image of a painted surface having a curved surface can be accurately evaluated by the evaluation model generated from the teacher data regarding the evaluated painted surface having a planar shape.

[0065] Each function shown in the above-described embodiment can be implemented by one or more processing circuits. The processing circuits include a programmed processor, an electric circuit, etc., and further include devices such as an application specific integrated circuit (ASIC) for a specific purpose, circuit components arranged to execute the described functions, etc.

[0066] The content of the present invention has been described above in accordance with the embodiments. However, it is obvious to those skilled in the art that the present invention is not limited to these descriptions, and various modifications and improvements are possible. It should not be understood that the discussion and drawings forming a part of this disclosure limit the present invention. Various alternative embodiments, examples, and operation techniques will become apparent to those skilled in the art from this disclosure.

[0067] The present invention naturally includes various embodiments and the like not described herein. Therefore, the technical scope of the present invention is defined only by the invention-specific matters according to the legitimate claims based on the above description.

Explanation of Reference Numerals

[0068] 11 Shape acquisition unit 13 Material acquisition unit 15 Strength acquisition unit 19 Light source unit 100 Controller 110 Strength correction unit 120 Evaluation model setting unit 130 Evaluation value estimation unit 400 Output unit

Claims

1. A shape acquisition unit that acquires shape information representing the curved shape of the painted surface, A light source unit that irradiates the painted surface with incident light having a first intensity distribution, An intensity acquisition unit that acquires a second intensity distribution of the reflected light from the painted surface, A painting evaluation device comprising a controller, The controller, Calculates a light scattering component on the painted surface included in the second intensity distribution based on the shape information due to the deviation from the planar shape of the curved shape, Sets the first intensity distribution associated with the reference intensity distribution so as to cancel out the calculated scattering component, Estimating an evaluation value corresponding to the second intensity distribution using an evaluation model that outputs an evaluation value of the distinctness of image of the painted surface for an input including the second intensity distribution A painting evaluation device characterized by the above.

2. The painting evaluation device according to claim 1, The smaller the deviation, the smaller the difference between the reference intensity distribution and the first intensity distribution, When the curved shape is a planar shape, the reference intensity distribution and the first intensity distribution coincide A painting evaluation device characterized by the above.

3. The painting evaluation device according to claim 1 or 2, The evaluation model, It is a learning model generated by machine learning based on teacher data that combines the intensity distribution of the reflected light obtained by irradiating a reference light having the reference intensity distribution on an evaluated painted surface having a planar shape and the evaluation value of the distinctness of image of the evaluated painted surface A painting evaluation device characterized by the above.

4. The painting evaluation device according to any one of claims 1 to 3, The evaluation value of the distinctness of image of the painted surface is an index determined by at least one of the smoothness of the painted surface, the ratio of diffuse reflection in the reflected light on the painted surface, and the resolution of the image reflected on the painted surface A painting evaluation device characterized by the above.

5. The painting evaluation device according to any one of claims 1 to 4, The shape acquisition unit acquires the design data of the painted surface as the shape information A painting evaluation device characterized by the above.

6. The painting evaluation device according to any one of claims 1 to 5, The shape acquisition unit acquires the measurement data obtained by measuring the painted surface as the shape information A painting evaluation device characterized by the above.

7. The painting evaluation device according to any one of claims 1 to 6, The reference intensity distribution has a periodic structure in the first direction A painting evaluation device characterized by the above.

8. The painting evaluation device according to claim 7, The reference intensity distribution has a periodic structure in a second direction different from the first direction A painting evaluation apparatus characterized by the above.

9. The painting evaluation apparatus according to any one of claims 1 to 8, wherein The controller Based on the shape information, calculates a main direction vector at a predetermined position on the painted surface, Sets an intensity distribution having a periodic structure in the direction of the main direction vector as the reference intensity distribution A painting evaluation apparatus characterized by the above.

10. The painting evaluation apparatus according to any one of claims 1 to 9, wherein The reference intensity distribution A first region where the intensity of the incident light is equal to or greater than a first threshold value, A second region where the intensity of the incident light is less than or equal to a second threshold value smaller than the first threshold value, Having A painting evaluation apparatus characterized by the above.

11. The painting evaluation apparatus according to any one of claims 1 to 10, wherein The shape acquisition unit Acquires position information of a region of the painted surface irradiated with the incident light, Acquires the shape information based on the position information A painting evaluation apparatus characterized by the above.

12. The painting evaluation apparatus according to any one of claims 1 to 11, wherein The light source unit irradiates the incident light including a first marking pattern, The intensity acquisition unit acquires, as a second marking pattern, an intensity distribution of reflected light from a region of the painted surface irradiated with the first marking pattern, The controller Based on the difference between the first marking pattern and the second marking pattern, acquires reference shape information of a region irradiated with the first marking pattern, Acquires position information of a region on the painted surface having the shape information that matches the reference shape information A painting evaluation apparatus characterized by the above.

13. The painting evaluation apparatus according to any one of claims 1 to 12, wherein Further includes a material acquisition unit that acquires material information of the painted surface, The controller Using the evaluation model that outputs an evaluation value of the distinctness of image of the painted surface with respect to an input including the material information and the second intensity distribution, estimates an evaluation value corresponding to the combination of the material information and the second intensity distribution A painting evaluation apparatus characterized by the above.

14. The painting evaluation apparatus according to claim 13, wherein The evaluation model A learning model generated by machine learning based on teacher data that combines the material information of the evaluated painted surface which is a planar shape, the intensity distribution of reflected light obtained by irradiating the evaluated painted surface with reference light having the reference intensity distribution, and the evaluation value of the distinctness of image of the evaluated painted surface A paint evaluation apparatus characterized by the above

15. Obtain shape information representing the curved shape of the painted surface Irradiate the painted surface with incident light having a first intensity distribution Obtain a second intensity distribution of the reflected light from the painted surface Calculate, based on the shape information, the light scattering component on the painted surface included in the second intensity distribution due to the deviation from the planar shape of the curved shape Set the first intensity distribution associated with the reference intensity distribution so as to cancel out the calculated scattering component Estimate the evaluation value corresponding to the second intensity distribution using an evaluation model that outputs an evaluation value of the distinctness of image of the painted surface for an input including the second intensity distribution A paint evaluation method characterized by the above

16. A shape acquisition unit that acquires shape information representing the curved shape of the painted surface, A light source unit that irradiates the painted surface with incident light having a first intensity distribution, An intensity acquisition unit that acquires a second intensity distribution of the reflected light from the painted surface, On a computer that controls these, A step of acquiring the shape information using the shape acquisition unit, Calculate, based on the shape information, the light scattering component on the painted surface included in the second intensity distribution due to the deviation from the planar shape of the curved shape, A step of setting the first intensity distribution associated with the reference intensity distribution so as to cancel out the calculated scattering component, A step of estimating the evaluation value corresponding to the second intensity distribution using an evaluation model that outputs an evaluation value of the distinctness of image of the painted surface for an input including the second intensity distribution, A paint evaluation program for causing the above to be executed

Images