Visualizing the appearance of at least two materials in a heterogeneous measurement environment

Abstract

A computer-implemented method for visualizing the appearance of at least two materials comprises obtaining a first set of appearance attributes, the appearance attributes in the first set being associated with a first material, the first set of appearance attributes comprising measured appearance attributes; obtaining a second set of appearance attributes, the appearance attributes in the second set being associated with a second material; and obtaining a geometric model of at least one virtual object, the geometric model defining a three-dimensional macroscopic surface geometry of the virtual object. The invention is characterized in that a third set of appearance attributes is synthesized from the first set of appearance attributes and the second set of appearance attributes, and a scene comprising the at least one virtual object is visualized using a display device, using the third set of appearance attributes, using the comparison set of appearance attributes and the geometric model, using the third set of appearance attributes to visualize a first portion of the at least one virtual object, and using the comparison set of appearance attributes to visualize a second portion of the at least one virtual object to allow a direct visual comparison of the first set of appearance attributes as modified by the second set of appearance attributes and the comparison set of appearance attributes.

Description

Technical Field

[0001] This invention relates to a method for visualizing the appearance of at least two materials. The invention also relates to apparatus for performing such a method and a corresponding computer program. Background Technology

[0002] Finding a color formulation that matches a given color to a physical object (“target object”) can be a lengthy trial-and-error process. A set of colorants is selected, candidate formulations using those colorants are searched or formulated, and test objects, such as test panels, are prepared by applying materials made according to the candidate formulations to the test objects. The test objects are then visually compared to the target object. If the color of the test object does not match the target object, the candidate formulation is repeatedly modified as needed until the color of the test object matches the target object within the desired tolerance range.

[0003] In some applications, the target object may be a vehicle component coated with an existing coating, and the goal is to find a coating formulation whose color matches the appearance of the existing coating. Even if the paint code or vehicle identification number associated with the target object is known, simply retrieving the corresponding reference formulation may not yield an acceptable match. This is because, even with paint code specifications and reference formulations, the color of a given target object will vary slightly depending on the batch, formulator, or year. Modifications to the reference formulation need to be considered.

[0004] Traditionally, the process of finding a matching formulation has been entirely manual. Whether formulating from scratch or modifying a reference formulation, success largely depends on the experience of the color professionals who select colorants and define candidate formulations. Even experienced experts often need several iterations until a satisfactory match is achieved.

[0005] Over the past few decades, increasingly sophisticated color matching software has been introduced to assist color professionals in achieving color matching within defined tolerance ranges with fewer iterations. Examples include "Color iMatch," software available from X-Rite, a company based in Grand Rapids, Michigan. TM The software available is PaintManager, from PPG Industries, Strongsville, Ohio; Match Pigment, from Datacolor, Lawrenceville, New Jersey; or Colibri, from Konica Minolta, Osaka, Japan. TMColorMatch. Color matching software typically has three main components (see, for example, [Ber1 9]): the first is a database of the optical properties of colorants. The second is a set of algorithms for selecting colorants and predicting candidate formulations. The third is a set of algorithms for correcting the initial candidate formulations when the match is not within tolerance.

[0006] To populate a database with the optical properties of colorants, these properties need to be determined through measurement. This can be done in different ways depending on the type of application. For example, for coatings commonly used in the automotive industry, mixtures of each colorant can be prepared with a base at different concentrations, and possibly with varying amounts of added white and black pigments. Reference objects can be created for each mixture in a so-called drawdown form (e.g., as a black and white opaque card coated with that mixture). The drawdown color properties, in the form of spectral data or in a predefined color space (e.g., trichromatic values ​​or CIELAB values), can then be determined using instruments such as a spectrophotometer and fed into the database.

[0007] To perform color matching on a physical target object, the color properties of the target object are also determined. The formulation software then predicts one or more candidate formulations, which are expected to produce color properties approximately identical to the target object. To evaluate the quality of the match, test objects (e.g., reductions in the case of coatings) can be created for the selected candidate formulations. The test objects can then be visually compared to the target object. If the match is still not visually satisfactory, the color properties of the test objects can be acquired, and the color formulation software can correct the formulation based on a comparison between the calculated color properties of the candidate formulations and the measured color properties of the test objects. This process can be repeated frequently as needed until an acceptable match is achieved.

[0008] While acceptable results can often be obtained with fewer iterations compared to traditional manual methods, there is still room for improvement. In particular, it is desirable to assess the quality of the match between the candidate formulation and the target material with increased confidence before the test subject has been actually produced. This is especially desirable if the location where the test subject is produced differs from the location where the target material is located. For example, the target material could be part of a damaged vehicle that needs repair in a body shop. However, the body shop itself may not have color mixing facilities. Instead, the body shop may have to order paint ready for application from a remote paint supplier. In such cases, a match within tolerance on the first shot may be necessary.

[0009] Known color matching methods may not yield satisfactory results, especially for painted coatings or other materials exhibiting gonioapparent properties. For example, some materials exhibit color flops. A color flop is a change in the color value, hue, or chromaticity of a material's appearance as the direction of illumination and observation changes. Other examples of materials with gonioapparent properties include those containing effect pigments, such as metallic flakes that produce a shimmering effect or interference flakes that produce a pearlescent effect, as well as materials with non-planar surface microstructures. In such cases, simply matching the color may be insufficient. Instead, matching the entire appearance, including angle-dependent color and texture, is required. Known techniques generally fail to provide satisfactory visualizations or measurements to accurately match the appearance of such materials.

[0010] US20150032430A1 discloses a method for digitally generating data via the use of a computer, the data indicating the synthetic appearance of a simulated material having physically plausible appearance properties. The method includes determining a dataset indicating the synthetic appearance of the simulated material based at least in part on data associated with a physically tangible source material and at least in part on data of measured properties of a physically tangible reference material.

[0011] US20150026298A1 discloses a method for selecting the most probable variant of a candidate paint color standard for vehicle repair using a mobile device. In this method, a user at a body shop inputs information about the vehicle's color into a mobile device, which transmits this information to a remote central computer. The central computer selects a candidate color standard and transmits information about that candidate color standard back to the mobile device. The mobile device displays information about the selected candidate color standard. The user then visually compares a physical image representing the selected candidate color standard with the color of the vehicle to be repaired. A drawback of this method is the need for a large number of physical images of the color standard at the body shop. Alternatively, an image of the selected candidate color standard can be displayed on a color display on the mobile device, and the user visually compares the displayed image with the color of the vehicle to be repaired. However, this requires careful calibration of the display to enable a meaningful comparison.

[0012] US20070097119A1 discloses a method for displaying a simulated paint coating on a display device. RGB color values ​​are calculated over a range of non-specular angles. A statistical texture function for the paint coating is determined. The statistical texture function is applied to the RGB values, and these values ​​are used to display the color pixels. The statistical texture function is independent of the illumination and viewing direction.

[0013] US20050128484A1 discloses a method for determining a repair paint formula for color matching. The color characteristics of the target color to be matched are identified, input, and processed in such a way that the target color can be visually displayed. Alternative colors are selected from a database. The alternative colors can be displayed on a monitor as several virtual sheets, each representing a different viewing angle, or displayed as a curved panel. An image showing the appearance characteristics of the sheet can be overlaid with the color. The virtual sheets can be viewed in conjunction with the target color. Interpolation can be performed on the colors and images obtained for multiple non-mirror angles to show the change in the sheet appearance with varying non-mirror angles. This document does not describe how the color and sheet appearance are interpolated.

[0014] Both US20080291449A1 and US20080235224A1 disclose methods for displaying images to select matching formulations to match the appearance of an article, such as a target coating on a vehicle. In one embodiment, color data of the article is obtained using a colorimeter or spectrophotometer. Texture data of the article is obtained using an imaging device. A target image is created based on the color and texture data and displayed on a display device. A preliminary matching formulation is manually retrieved from a database. A matching image is generated for the preliminary matching formulation. The matching image and the target image are displayed side-by-side on the display device. The target image and the matching image can be displayed for multiple non-mirror angles. The aforementioned documents do not mention how to generate and display texture data for multiple non-mirror angles.

[0015] US20200089991A1 discloses a system for displaying one or more images to select one or more matching formulations to match the appearance of a target coating on an article. A first database contains repair formulations and associated appearance characteristics. A second database contains identification information or three-dimensional geometric data of at least one article. A preliminary matching formulation is retrieved from the first database, an article or its three-dimensional geometric data is selected from the second database, and marked portions of the article's surface are received. An individual matching image containing the marked portions and unmarked portions adjacent to the marked portions is generated and displayed on a display device. In the marked portions, the individual matching image is generated based on the appearance characteristics of the preliminary matching formulation. In the unmarked portions, the individual matching image is generated based on the appearance characteristics of the article. Appearance characteristics are calculated from images acquired from the article using an imaging device. Appearance characteristics may include texture, metallic or pearlescent effects, gloss, image sharpness, flake appearance (such as texture, glitter, reflection, and shimmer), and enhancement of depth perception imparted by the flakes. This document does not specify how the appearance of the article is displayed for different illumination and viewing directions.

[0016] US8,872,811B1 discloses a method for digitally generating data that indicates the synthetic appearance of a simulated material having physically plausible appearance properties. The dataset indicating the synthetic appearance of the simulated material is determined partly based on data associated with a physically tangible source material and partly based on data of measured properties of a physically tangible reference material.

[0017] US2007291993A1 discloses an apparatus for measuring the spatial undersampled bidirectional reflection distribution function (BRDF) of a surface. Summary of the Invention

[0018] The object of this invention is to provide a method for visualizing the appearance of target materials and candidate materials included in a target object, wherein the formulation of the candidate materials has been determined by formulation software in such a way that the user can determine with increased confidence whether the appearance of the materials matches, without the need to produce physical test objects.

[0019] In a preferred embodiment, a computer-implemented method for visualizing the appearance of at least two materials includes: obtaining a first set of appearance attributes, the appearance attributes in the first set being associated with a first material, the first set of appearance attributes including measured appearance attributes; obtaining a second set of appearance attributes, the appearance attributes in the second set being associated with a second material; and obtaining a geometric model of at least one virtual object, the geometric model defining the three-dimensional macroscopic surface geometry of the virtual object. The invention is characterized by synthesizing a third set of appearance attributes from the first set of appearance attributes and the second set of appearance attributes, and using a display device, the third set of appearance attributes, a comparison set of appearance attributes, and the geometric model to visualize a scene including the at least one virtual object; using the third set of appearance attributes to visualize a first portion of the at least one virtual object; and using the comparison set of appearance attributes to visualize a second portion of the at least one virtual object, thereby allowing direct visual comparison between the first set of appearance attributes, as modified by the second set of appearance attributes, and the comparison set of appearance attributes.

[0020] In one example, the first set of appearance attributes includes a color attribute but no texture attribute, the second set of appearance attributes includes both color and texture attributes, and the third set of appearance attributes is a combination of the color attribute from the first set of appearance attributes and the texture attribute from the second set of appearance attributes.

[0021] In another example, the first set of appearance attributes includes a first plurality of color attributes corresponding to a first plurality of optical measurement geometries, the second set of appearance attributes includes a second plurality of color attributes corresponding to a second plurality of optical measurement geometries, the second plurality of measurement geometries being larger than the first plurality of measurement geometries; and the third set of appearance attributes includes a composite of color attributes from the first plurality of color attributes and the second plurality of color attributes. In the third set of appearance attributes, at a measurement geometry matching the first set of color attributes, a color attribute in the second plurality of color attributes can be replaced by the first plurality of color attributes. In one example, color attributes in the second plurality of color attributes that are not replaced by the first plurality of color attributes are corrected to adjust for the color difference between the replaced color attribute and the replaced color attribute.

[0022] In some embodiments, the method further includes generating a first instance of an appearance model, the first instance of the appearance model including a third set of appearance attributes; and generating a second instance of the appearance model, the second instance of the appearance model including a comparison set of appearance attributes. The appearance model includes a discrete texture table comprising a plurality of target textures, each target texture represented by image data and associated with a different set of target coordinates indicating a specific combination of illumination and viewing directions, and the first and second instances of the appearance model are used to visualize the at least one virtual object. The step of generating the first instance of the appearance model may further include at least one of the following operations: interpolating between available appearance attributes under different combinations of illumination and viewing directions; and extrapolating from available appearance attributes under selected combinations of illumination and viewing directions. In this example, available appearance attributes include appearance attributes from the third set of appearance attributes.

[0023] In one example of this embodiment, the first material includes target automotive paint, and the first set of appearance attributes includes the measured appearance attributes of the target automotive paint; the second material includes reference automotive paint, and the second set of appearance attributes includes the measured appearance attributes of the reference automotive paint; and the comparison set of appearance attributes includes the second set of appearance attributes.

[0024] In another example of this embodiment, the first material includes the target automotive paint, and the first set of appearance attributes includes the measured appearance attributes of the target automotive paint; the second material includes a candidate formulation of the automotive paint, and the second set of appearance attributes includes the calculated appearance attributes of the candidate formulation; and the comparison set of appearance attributes includes the second set of appearance attributes.

[0025] In another example of this embodiment, the first material includes the test object automotive paint, and the first set of appearance attributes includes the measured appearance attributes of the test object; and the second material includes the reference automotive paint, and the second set of appearance attributes includes the measured appearance attributes (54) of the reference automotive paint; and the comparison set of appearance attributes includes the second set of appearance attributes.

[0026] In another example of this embodiment, the first material includes a candidate automotive paint formulation, and the first set of appearance attributes includes the measured appearance attributes of the candidate automotive paint formulation; the second material includes a target automotive paint, and the second set of appearance attributes includes the measured appearance attributes of the target automotive paint, and the comparison set of appearance attributes includes the second set of appearance attributes.

[0027] The third set of appearance attributes may also include appearance attributes from the fourth set of appearance attributes. In one example, the fourth set of appearance attributes includes reduced appearance attributes from one or more pigments associated with paint formulation.

[0028] In another example, the second set of appearance attributes includes reduced appearance attributes from one or more pigments associated with the paint formulation. In another example, the first material includes a target automotive paint, and the paint formulation includes a reference standard for the target automotive paint. In another example, the second set of appearance attributes includes calculated appearance attributes corresponding to one or more pigments associated with the paint formulation.

[0029] As used in this disclosure, the term “appearance” includes both color and texture, with “texture” being broadly understood as describing the spatial variation in appearance across the surface of an object.

[0030] Texture can be highly dependent on the direction of illumination and observation. For example, in metallic effect paints that typically contain highly reflective flakes, the location of the surface where strong reflection is observed may vary discontinuously when the direction of illumination and / or observation changes continuously, because the flakes at different locations across the surface will reflect under different combinations of illumination and observation directions. Therefore, the observed texture can be very different between illumination and observation directions that differ by only small angular values. Thus, this invention is particularly useful for matching materials containing reflective flakes.

[0031] In some embodiments, the first set of appearance properties associated with the target material may include measured appearance properties of the target material. The proposed method may include the step of actually performing measurements on a target object including the target material using an appearance capture device.

[0032] In some embodiments, a second set of appearance attributes associated with a candidate material may include candidate appearance attributes. In some embodiments, the candidate material is a known reference material, and therefore the candidate appearance attributes are appearance attributes associated with the known reference material. In other embodiments, the candidate material is composed of multiple component materials, and appearance attributes may be available for multiple reference materials including the component materials of the candidate material. Thus, by using calculations, candidate appearance attributes may be based on predetermined appearance attributes associated with multiple reference materials. A formulation can be determined in such a way that the candidate material has expected appearance attributes that at least approximately match the appearance attributes of the target material. In particular, the proposed method may include the step of determining a formulation. This may involve minimizing a measure of difference between the expected appearance attributes of the candidate material and the measured appearance attributes of the target material. Suitable measures of difference and optimization algorithms are well known in the field of color formulation.

[0033] In some embodiments, the method further includes, for example, obtaining a physical, tangible test object comprising candidate materials by producing test objects. The method may include determining measured appearance properties of the test object by performing measurements on the test object using an appearance capture device. The method may also include visualizing at least a portion of the at least one virtual object using the measured appearance properties of the test object. In this way, the appearance of the test object can be compared to the appearance of the target object even if the target object does not exist in the same location as the test object. Additionally or alternatively, the method may include determining a modified formulation using the measured appearance properties of the test object and the calculated appearance properties of the candidate materials. Once the measured appearance properties of the test object are available, suitable algorithms for determining the modified formulation are well known in the field of color formulation. Such algorithms are often referred to as “correction algorithms.” The method can then be repeated with the modified formulation.

[0034] The appearance capturing device used to determine the measured appearance attributes of the test subject can be the same as the device used to determine the measured appearance attributes of the target object, or it can be a different device. The device is preferably configured to determine the same type of appearance attribute under the same illumination and observation conditions as the appearance capturing device used for the target object. However, it is also conceivable that the appearance capturing device for the test subject can be configured to determine appearance attributes of different types, different numbers of appearance attributes, and / or different illumination and observation directions than the appearance capturing device used for the target object.

[0035] In each case, the appearance capturing device can be a multi-angle spectrophotometer configured to determine color attributes for multiple combinations of illumination and observation directions. In addition to color attributes, the appearance capturing device can also have imaging capabilities to determine texture attributes.

[0036] In some embodiments, the measured appearance properties of the target material include multiple measured image datasets, each associated with a different combination (e.g., different pairs) of illumination and viewing directions. For this purpose, an appearance capture device for determining the appearance properties of the target material may include one or more cameras configured to determine image data for multiple combinations (e.g., multiple pairs) of illumination and viewing directions. In some embodiments, all combinations of illumination and viewing directions of the measured image data include the same viewing direction but multiple different illumination directions (or equivalently, the same illumination direction and multiple different viewing directions). This may be the case, for example, when using an appearance capture device with a single camera and multiple different light sources. In other embodiments, the combination of illumination and viewing directions includes two or more viewing directions.

[0037] In some embodiments, the measured appearance properties of the target material may have no texture properties, or may contain only a limited set of texture properties. This may occur, for example, if the appearance capture device used to measure the target object lacks imaging capability or has only very limited imaging capability. In this case, the measured appearance properties of the target material may be supplemented by texture properties from different sources. Specifically, the first set of appearance properties (i.e., the set associated with the target material) may include texture properties, particularly texture properties in the form of image data, which have been determined based on texture properties associated with one or more reference materials and / or with candidate materials. Specifically, some or all of the texture properties in the first set may be determined based on calculated texture properties associated with candidate materials (particularly in the form of image data) or based on measured texture properties associated with test objects including candidate materials (particularly in the form of image data). Therefore, the method may include determining texture properties (particularly in the form of image data) in the first set of appearance properties based on texture properties associated with one or more reference materials and / or with candidate materials (particularly in the form of image data). This may involve modifying the pixel values ​​of the image data associated with the reference materials and / or candidate materials. In this way, the target material can be visualized as having a realistic texture, even if the measured texture information itself is insufficient to achieve this purpose.

[0038] In other embodiments, the available appearance attributes of the candidate or reference materials may lack texture attributes, or may contain only a limited set of texture attributes. This may occur, for example, if the database containing the appearance attributes of the reference materials was created using a simpler or older appearance capture device lacking imaging capabilities or having only limited imaging capabilities. In this case, the appearance attributes in the second set (i.e., the set associated with the candidate materials) may be supplemented by texture attributes (particularly in the form of image data) from different sources. Specifically, the second set of appearance attributes may include texture attributes (particularly in the form of image data) already determined based on measured texture attributes (particularly in the form of image data) associated with the target material and / or with test subjects including the candidate materials. In particular, some or all of the texture attributes in the second set may be determined based on measured texture attributes (particularly in the form of image data) associated with the target material or based on measured texture attributes (particularly in the form of image data) associated with test subjects including the candidate materials. Therefore, the method may include determining texture attributes in the second set of appearance attributes based on measured texture attributes associated with the target material and / or based on texture attributes associated with test subjects including the candidate materials. This can involve modifying pixel values ​​in image data associated with the target material and / or the test subject. In this way, candidate materials can be visualized as having a realistic texture, even if the available texture information itself is insufficient to achieve this purpose.

[0039] In a preferred embodiment, the method includes:

[0040] Generate the first instance of the appearance model, which includes a first set of appearance attributes; and

[0041] Generate a second instance of the appearance model, which includes a second set of appearance attributes.

[0042] The appearance model includes a discrete texture table comprising multiple target textures, each represented by image data and associated with a different set of target coordinates indicating a specific combination of illumination and viewing directions.

[0043] The first and second instances of the appearance model are used to visualize the at least one virtual object.

[0044] While the simplified embodiment of the appearance model only describes the correlation between color and illumination and viewing conditions without considering spatial variations in appearance across the surface of an object, in this context, the appearance model is preferably a model that includes texture in addition to the angular correlation of reflection and / or transmission. Specifically, the appearance model can be a spatially varying two-way reflectance distribution function (“SVBRDF”) model, a two-way texture function (“BTF”) model, a two-way surface scattering distribution function (“BSSRDF” model”), a specialized model for automotive paint, etc. Many such models are known in the art.

[0045] Advantageously, the appearance model describes texture based on a discrete texture table. This texture table includes multiple textures, each represented by image data and associated with a different set of coordinates that indicates a specific combination of illumination and viewing directions.

[0046] Appearance models often require far more appearance attributes than the limited set of measured or predetermined appearance attributes actually available for the target material, candidate material, reference material, or test material, respectively. Furthermore, appearance models may require appearance attributes of different types than those available. In particular, the set of appearance attributes required for an appearance model can have a larger cardinality (i.e., contain a larger number of appearance attributes) than a limited set of available appearance attributes. In other words, the set of available appearance attributes may be sparse compared to the denser set of appearance attributes required for the appearance model. Mathematical operations, possibly involving transformations, fitting operations, and / or interpolation and extrapolation, may be necessary for instances generating appearance models from a limited set of available appearance attributes. For example, a limited set of available appearance attributes may contain color attributes (e.g., in the form of tricolor or spectral data) only for a limited number of illumination and observation direction pairs. This limited set of available appearance attributes may also include texture attributes (e.g., in the form of image data) only for a pair or a limited number of illumination and observation direction pairs. In contrast, appearance models can describe reflection and / or transmission as functions of angle and position in different forms. In particular, appearance models may require spectral and / or texture data for a much larger number of illumination and observation direction pairs than are available, and the required illumination and observation directions may differ from those available. Below, examples of how to generate instances of appearance models with a dense set of appearance attributes from a sparse set of available appearance attributes will be described in more detail.

[0047] Generating the first and / or second instance of the appearance model may include at least one of the following operations:

[0048] Interpolation between available appearance attributes under different combinations of illumination and observation directions; and

[0049] Extrapolate the available appearance attributes from the selected combination of illumination and observation directions.

[0050] Available appearance attributes may include multiple source textures, each associated with a different set of source coordinates, each set indicating a combination of illumination and viewing directions, for which the corresponding source texture indicates spatial variations in appearance. Source textures may be associated with source coordinate sets different from the target coordinates of a discrete texture table. Generating a first and / or second instance of the appearance model may then include determining at least one of the target textures by performing a statistical texture synthesis operation based on:

[0051] (i) The target coordinate set associated with the target texture;

[0052] (ii) Source material, and

[0053] (iii) Source coordinate group.

[0054] Each source and target coordinate set indicates a specific combination of illumination and observation directions. Each source and target coordinate set may, in particular, be a set of two or more angular coordinates. The source and target coordinates do not need to directly indicate the illumination and observation directions in a reference frame defined by the surface of the material. Instead, they can be derived from these directions through appropriate transformations. In particular, the source and / or target coordinates can be expressed using Rusinkiewicz parameterization [Rus98]. Specifically, in Rusinkiewicz parameterization, each source or target coordinate set may include polar angles or consist of polar angles.

[0055] Each source texture and each target texture can be represented by two-dimensional image data, typically in the form of an array of pixels, each pixel having a pixel value representing the reflection at that pixel's location. The source and target textures do not need to be the same size. Each source texture can represent a spatial variation of the material's reflective properties for a specific combination of illumination and viewing directions. Similarly, a target texture can represent a spatial variation of the material's reflective properties for another combination of illumination and viewing directions. This spatial variation can represent, for example, the effect of an effect pigment in the material, which produces a shimmering effect.

[0056] By performing a statistical texture compositing operation, a target texture is created that has similar statistical properties to the source texture, but is dissimilar to any source texture and the target texture at different target coordinates on a pixel-by-pixel basis. This mimics the properties of metallic paint.

[0057] Statistical texture synthesis operations may include:

[0058] (i) Assign interpolation weights to each element in the source texture based on the target and source coordinates; and

[0059] (ii) Use the source texture and the assigned interpolation weights to synthesize the target texture.

[0060] In some embodiments, assigning interpolation weights to each of the source textures includes the following process:

[0061] Create the Delaunay triangulation of the source coordinate group;

[0062] Find a simplex with multiple angles within the Delaunay triangulation containing the target coordinates; and

[0063] The target coordinates are used as the centroid coordinates of the found simplex as the interpolation weights of the source texture at the corner of the found simplex.

[0064] The interpolation weight of the source textures found outside the simplex is preferably set to zero, that is, the source textures found outside the simplex are not used to synthesize the target texture.

[0065] Synthesizing the target texture may include the following steps:

[0066] (a) Randomly select one of the source textures with a probability proportional to the interpolation weight of the source texture;

[0067] (b) Randomly extract texture patches from the selected source texture, where the texture patches are a part of the source texture;

[0068] (c) Modify the extracted texture by modifying the pixel values ​​in the extracted texture to obtain a modified texture, wherein the pixel value modification is performed in such a way that at least one statistical property of the modified texture approximates the corresponding average statistical property, and the average statistical property is determined by performing a weighted average on the source texture, which is weighted by interpolation weights.

[0069] (d) Insert the modified texture patch into the target texture so that the modified texture patch seamlessly fits the texture content already existing in the target texture; and

[0070] (e) Repeat steps (a)-(d) until the target texture is completely filled.

[0071] Each texture patch should be understood as a continuous portion of the source texture from which it has been extracted, i.e., a continuous image patch that has been cut out from the source texture. Each texture patch can have any shape. Rectangular shapes are preferred, and in particular square shapes. Each texture patch preferably has a side length that corresponds to at least twice the size of the pigment with the greatest effect in the paint being modeled (e.g., the largest reflective flake). On the other hand, preferably, each texture patch has an area not greater than 10% of the area of ​​the corresponding source texture.

[0072] Texture patches are extracted randomly, meaning the position of each extracted texture patch within the selected source texture is randomly determined, preferably with a constant weight over the area of ​​the source texture. By randomly extracting texture patches, it is ensured that the target textures obtained at different target coordinates are dissimilar.

[0073] In a preferred embodiment, the statistical property for the expected approximate match is a pixel value histogram. A pixel value histogram is a dataset that includes an indicator of the relative frequency of each of a range of brightness values ​​that may exist in an image. Pixel value histograms are widely used in digital image processing to visualize properties such as contrast and brightness of digital images. The generation of them from image data and their interpretation are well known in the art.

[0074] Modifying pixel values ​​preferably involves applying a pointwise transformation to the pixel value, i.e., a transformation applied individually to each pixel value, where the pointwise transformation is monotonically non-decreasing. In this way, it is ensured that pixel values ​​identical before modification remain identical after modification, and that the relative brightness order of pixels does not change; that is, a pixel value brighter than another pixel value before modification will not be darker than another pixel value after modification. Suitable transformation functions are well-known in the field of digital image processing. In particular, histogram matching algorithms for determining transformation functions that will match the histogram of one image with the histogram of another image are well-known in the art.

[0075] The modified texture piece is inserted into the target texture so that it seamlessly fits the texture content already existing in the target texture. This can be achieved using techniques well-known in the art, such as Graphcut / MinCut [Kw03]. In these techniques, a seam is calculated that enhances the visual smoothness between the existing texture content and the newly placed piece. The texture piece and the existing texture content are stitched together along this seam.

[0076] In addition to discrete texture surfaces, appearance models can include monochromatic luminance BRDF models to describe the angular correlation of overall reflectance properties averaged at surface location and wavelength. Instances of generating appearance models of materials can then include determining the parameters of the monochromatic luminance BRDF model.

[0077] The appearance model may also include a discrete color table with multiple entries in the form of color values, each entry associated with a specific set of coordinates, each set indicating the illumination and observation direction of the observed material. The color values ​​can be expressed in any color space, such as the RGB or CIEXYZ tri-color space, or any other color space such as CIELAB (L*a*b*), or in any format as spectral data representing the material's spectral response to incident light. Generating an instance of the appearance model can then include at least one of the following operations:

[0078] (i) Determining entries for the discrete color table by interpolation between available color attributes under different combinations of illumination and viewing directions; and / or

[0079] (ii) The entries for the discrete color table are determined by extrapolating the available color attributes from different combinations of illumination and observation directions.

[0080] Instead of a monochrome BRDF model associated with a color table, in alternative embodiments, the appearance model may include any other kind of model used to describe the angular correlation between brightness and color. For example, the appearance model may include a tri-color BRDF model that models the angular correlation of reflectance properties for each of the three distinct color channels in a tri-color color space (such as CIEXYZ or RGB), or that models the angular correlation of L*a*b* values ​​in the CIELAB color space.

[0081] The appearance model can also include a model with the effect of a transparent coating on top of an opaque or semi-transparent paint layer.

[0082] In some embodiments, the scene is visualized using defined illumination conditions, which, as is well known in the art, can be described, for example, by an environment map. The method may include changing the illumination conditions, particularly one or more illumination directions and / or the type of light source, i.e., the spectral properties of the light used for illumination, and visualizing the virtual object with the changed illumination conditions to facilitate comparison of the appearance of first and second materials under different illumination conditions. For example, in this way, metamerism can be detected before the production of test objects.

[0083] In some embodiments, the at least one virtual object is visualized in a defined direction, and the method includes changing the orientation of the virtual object, i.e., rotating the virtual object, and visualizing the virtual object in the orientation presented after the change in orientation. This allows for better discernment of subtle differences in appearance that depend on the direction of illumination and observation. The visualization can be performed in the form of video, which visualizes the continuously rotating virtual object. The method may also include changing the size and / or shape of the virtual object.

[0084] In some embodiments, the method includes:

[0085] Visualize the at least one virtual object together with virtual separator elements.

[0086] The at least one virtual object has first and second parts that are adjacent to each other, and the first and second parts are visually separated by the virtual separating element in such a way that the first and second parts appear to meet at the location of the virtual separating element.

[0087] The first instance of the appearance model is used to visualize the first part of the at least one virtual object, and

[0088] The second instance of the appearance model is used to visualize the second part of the at least one virtual object.

[0089] This method may include moving virtual divider elements relative to a virtual object. This can be done interactively, i.e., in response to user input, such as moving a pointing device like a mouse or trackpad, pressing a key on a keyboard, or waving a finger or pointing device like a digital pencil across a touchscreen. When the virtual divider element is moved, the first and second parts are permanently redefined in such a way that, when the virtual divider element is moved, the first and second parts still appear to meet at the location where the virtual divider element was. In this way, when the material of the virtual object changes, the user can visually assess how the appearance of the virtual object changes in the selected area of ​​the virtual object.

[0090] In some embodiments, the first and second materials may be substantially opaque materials, such as those used in the automotive industry for coating vehicle parts, or opaque plastics. In other embodiments, the first and second materials may be translucent materials, such as translucent plastics. The appearance model may be particularly suited to a specific category of materials. For example, for vehicle coatings, the appearance model may include a model of the effect of a transparent coating on top of an opaque or translucent paint layer and / or a model of the effect of reflective flakes in the paint layer. As another example, for translucent materials, the appearance model may use volumetric absorption and scattering coefficients, as well as phase function parameters, to model the subsurface light transport of the material in order to solve the radiative transfer equation.

[0091] The display device can be a screen, such as an LCD screen, a projector, or any other type of display device as is well known in the art. If the display device includes a screen, the screen can be touch-sensitive, as is well known in the art. The display device can be capable of creating 3D impressions, as is well known in the art. For example, the display device can be a VR headset or a 3D display. In such a case, the virtual dividing element can be a virtual plane. However, in a preferred embodiment, the display device visualizes the virtual object as a two-dimensional projection onto a two-dimensional viewing plane. In this case, the virtual dividing element can be a simple line, particularly a straight line, on the viewing plane.

[0092] This disclosure also provides a system for visualizing the appearance of at least two materials. The system includes a display device, at least one processor, and at least one memory, the memory including program instructions configured to use the display device to cause the processor to perform the methods described above. The system may also include the first and / or second appearance capturing devices described above, and may be accordingly configured to receive appearance attributes from these appearance capturing devices.

[0093] This disclosure also provides a computer program product including program instructions that, when executed by a processor, cause the processor to perform the methods described above. The computer program product may include a non-volatile computer-readable medium on which the program instructions are stored. The non-volatile medium may include a hard disk, a solid-state drive, a memory card, or any other type of computer-readable medium as is well known in the art.

[0094] In particular, program instructions may include rendering software configured to perform the following steps:

[0095] Receive the first and second sets of appearance attributes;

[0096] Receive three-dimensional geometric data representing the continuous three-dimensional surface geometry of the at least one virtual object; and

[0097] The scene, including the at least one virtual object, is rendered based on the first and second sets of appearance attributes and the three-dimensional geometric data.

[0098] The rendering software can be configured to receive first and second sets of appearance attributes in the form of first and second instances of the appearance model.

[0099] The program instructions may also include model generation software configured to perform the following steps:

[0100] Receive available appearance attributes; and

[0101] Based on the available appearance attributes, at least one of the following operations is used to determine the instance of the appearance model:

[0102] Interpolation between available appearance attributes under different combinations of illumination and observation directions; and

[0103] Extrapolate the available appearance attributes from the selected combination of illumination and observation directions.

[0104] The program instructions may also include formulation software configured to perform the following steps: using predetermined appearance properties associated with a plurality of reference materials and using a formulation to calculate appearance properties associated with a candidate material. The formulation software may also be configured to determine a formulation in such a way that the candidate material has expected appearance properties that at least approximately match the appearance properties of the target material. Attached Figure Description

[0105] Preferred embodiments of the invention are described below with reference to the accompanying drawings, which are for the purpose of illustrating the presently preferred embodiments of the invention and not for the purpose of limiting the presently preferred embodiments of the invention. In the drawings,

[0106] Figure 1 A schematic diagram illustrating a method for visualizing the appearance of two materials is shown;

[0107] Figure 2 The illustration is shown. Figure 1 Flowchart of the method;

[0108] Figure 3 A schematic diagram of an exemplary color mixing system is shown, representing a hardware-oriented illustration.

[0109] Figure 4 shows a perspective view of an exemplary appearance capture device according to the prior art;

[0110] Figure 5 shows a perspective view of the measurement array of the appearance capture device in Figure 4;

[0111] Figure 6 A diagram illustrating an exemplary discrete color table is shown;

[0112] Figure 7 An example texture of a discrete texture table is shown;

[0113] Figure 8 A diagram illustrating an exemplary discrete texture table is shown;

[0114] Figure 9 A schematic diagram of a method for generating a target texture based on multiple source textures is shown.

[0115] Figure 10 A schematic histogram of pixel values ​​is shown;

[0116] Figure 11This diagram illustrates the insertion of a texture sheet into a target texture.

[0117] Figure 12 A flowchart illustrating a method for generating instances of appearance models is shown;

[0118] Figure 13 A flowchart is shown for a method to generate a target texture based on multiple source textures; and

[0119] Figure 14 This diagram illustrates how the information content varies across three dimensions.

[0120] Figure 15 A diagram illustrating a second exemplary discrete color table is shown;

[0121] Figure 16 A flowchart is shown for editing a set of appearance attributes. Detailed Implementation

[0122] definition

[0123] In this disclosure, references in the singular form may also include the plural. Specifically, unless the context otherwise indicates, the words “a” or “one” may refer to one or more.

[0124] The term "colorant" should be understood as a component of a material that provides the appearance of color when light is reflected from or transmitted through the material. Colorants include pigments and dyes. A "pigment" is a colorant that is generally insoluble in the base material. Pigments can be from natural or synthetic sources. Pigments can contain both organic and inorganic components. The term "pigment" also includes so-called "effect pigments" that produce special effects in materials. Examples include interference pigments and reflective particles or flakes. A "dye" is a colorant that is generally soluble in the base material.

[0125] The term "formulation" should be understood as a collection of information relating to determining how a material should be prepared. Materials can include coating materials (such as automotive paint), solid materials (such as plastic materials), semi-solid materials (such as gels), and combinations thereof. A formulation specifically includes the concentration of ingredients from which the material is composed, such as binders and colorants. Materials already prepared according to a formulation may also be referred to as a "formulation".

[0126] The term "visual appearance," or simply "appearance," should be broadly understood as the way an object reflects and transmits light, including but not limited to how individuals observing the object perceive its color and surface texture under various viewing conditions. Appearance also includes instrumental measurements of how an object reflects and transmits light.

[0127] One aspect of visual appearance is color. The “color” of an object is determined by the portion of the spectrum of incident white light that is reflected or transmitted but not absorbed. The color of an object can be described by “color attributes.” Generally, color attributes indicate the spectral response of an object when it is illuminated by incident light. In the context of this disclosure, the term “color attribute” is to be understood broadly as including any form of data indicating the spectral response of an object when it is illuminated by incident light. Color attributes can take the form of color values ​​in any color space, such as in a three-color space like RGB or CIEXYZ, or in any other color space like CIELAB (L*a*b*), or in the form of spectral data representing the spectral response of a material to incident light in any format. In the context of this disclosure, color attributes may in particular include reflectance values ​​and / or absorption and scattering coefficients of a material at multiple wavelengths. A “discrete color table” is to be understood as involving a set of multiple sets of color attributes, each set of color attributes being associated with different combinations of illumination and viewing directions.

[0128] Another aspect of visual appearance is texture. The term "texture" is broadly understood to refer to spatial variations in the appearance of a material's surface, both at microscopic or mesoscopic scales (i.e., scales at which individual structural elements are typically not perceptible to the naked eye) and at macroscopic scales (i.e., scales at which individual structural elements are perceptible to the naked eye). Texture, as understood in this disclosure, includes phenomena such as roughness, luster, and surface morphology. Texture can be described by "texture properties." In the context of this disclosure, the term "texture property" is broadly understood to include any form of data capable of quantifying at least one aspect of texture. Examples of texture properties include global texture properties, such as global roughness parameters or global luster parameters. In some embodiments, texture properties may include normal maps or height maps. In some embodiments, texture properties may include image data. In some embodiments, image data may be associated with a specific combination of illumination and viewing directions. In such embodiments, texture properties may include multiple image datasets, each associated with a different combination of illumination and viewing directions. A “discrete texture table” should be understood as a collection of multiple sets of texture attributes, preferably in the form of image data, each set of texture attributes being associated with different combinations of illumination and viewing directions. In some embodiments of this disclosure, the images in the discrete texture table are generated from a set of source textures, and these images are therefore referred to as “target textures”.

[0129] The term "appearance model" should be understood as a formal construction involving the mathematical description of appearance using multiple material-related parameters called "appearance attributes." Appearance attributes can include color attributes and texture attributes. The appearance model is preferably independent of the apparatus and platform; that is, it is independent of the specific measuring device that may have been used to determine the appearance attributes, and it is independent of the specific rendering platform used for visualization. The appearance model provides a mathematical description of the appearance in such a form and with such a level of completeness that it is possible to generate visualizations (i.e., rendering and display) of virtual objects under arbitrary lighting and viewing conditions using the appearance model in conjunction with the geometric model of the virtual object. For any part of the surface of the virtual object whose geometry is defined by the geometric model, and for any given lighting and viewing angle, the appearance model provides the necessary information to calculate the appropriate appearance attributes.

[0130] A "virtual object" is an object that exists only virtually in a computer. A virtual object may or may not correspond to a real, tangible object. Virtual objects can be mathematically defined through geometric models and associated appearance information.

[0131] A "geometric model" of a real or virtual object is to be understood as at least an approximate representation of the geometry of any surface of that object in three dimensions. For example, in some embodiments, the geometric model defines curves along at least two mutually orthogonal directions. In other embodiments, the geometric model defines multiple polygons or faces. The geometric model may be represented, for example, by a CAD file.

[0132] The term "instance of appearance model" should be understood as a set of values ​​for all appearance attributes relating to a particular appearance model, supplementing information that allows identification of the underlying appearance model. In other words, while the appearance model itself defines the formal construction of how an appearance is described based on a set of appearance attributes, instances of the appearance model include, for example, actual values ​​of these appearance attributes specific to a given material, such as those determined by measurement and / or derived from appearance attributes of individual components of the material. In particular, instances of the appearance model can be provided in the form of data files. Preferably, the data files are in a device and platform-independent format, such as AxF proposed by X-Rite. TM Format. Therefore, an AxF file can be considered a representation of an "instance" of the appearance model. However, this disclosure is not limited to a specific format for instances of the appearance model, and instances of the appearance model may be provided in another file format (e.g., MDL format), or it may even be provided in a form different from a file, such as as a data stream.

[0133] Luminance properties can be represented in an appearance model with a bidirectional reflectance distribution function (BRDF). A BRDF is generally understood as a function defining how light is reflected at an opaque surface depending on the direction of illumination and observation, thus providing the ratio of reflected radiation emitted along the observation direction to the irradiance incident on the surface from the direction of illumination. If the surface exhibits spatial variation in this ratio, the BRDF is understood to provide an average of this ratio over a region of the surface. A monochromatic luminance BRDF is a BRDF that provides a (potentially weighted) average of the ratio across all visible wavelengths, thus modeling the overall luminance variation of the surface.

[0134] "Image data" is data representing an image. An image includes images of an actual target surface or object, as well as composite images derived from one or more geometric models combined with one or more sets of appearance attributes. An image may take the form of a two-dimensional array of image elements ("pixels"), each pixel having a specific pixel value. Pixel values ​​may represent the reflection at the pixel's location at a specific wavelength, the average reflection within a specific wavelength range, or the average reflection across all visible wavelengths. Therefore, in some embodiments, image data may be provided as an array of pixel values. In other embodiments, image data may be provided in a compressed or transformed form. An "image dataset" is image data comprising at least one image or a dataset consisting of image data of at least one image, i.e., a dataset representing one or more images.

[0135] If the pixel values ​​of two images X and Y of the same size are uncorrelated, they are considered "dissimilar" on a pixel-by-pixel basis. Dissimilarity can be quantified by a correlation measure of pixel values ​​that is appropriately defined. For the purposes of this disclosure, if the Pearson product-moment correlation coefficient r of pixel values ​​in an image... XY If the absolute value of the correlation coefficient is no greater than 0.1, preferably no greater than 0.02, then the images can be considered "dissimilar". The Pearson product-moment correlation coefficient r... XY The quotient is defined as the product of the covariance of pixel values ​​and the standard deviation (i.e., the square root of the variance) of the pixel values ​​of the two images:

[0136]

[0137] Here, x i Represents the pixel values ​​in image X. y represents the arithmetic mean of the pixel values ​​in image X. i Represents the pixel value in the Y region of the image. This represents the arithmetic mean of the pixel values ​​in image Y, where N indicates the number of pixels in each image (which is the same for both images since they are the same size), and the summation is performed over all pixels. The arithmetic mean of the pixel values ​​in image X. Defined in the usual way

[0138]

[0139] The "Rusinkiewicz parameterization" describes the illumination and observation directions based on the "halfway vector" and the "difference vector." The "halfway vector" is defined as the vector midway between the incident and reflected rays, and the "difference vector" is the illumination direction in a reference frame where the halfway vector is located at the north pole. The spherical coordinates of the halfway vector in the sample-fixed reference frame where the surface normal is located at the north pole can be expressed as (θ... h φ h ), where θ h This is called the "mid-range angle" in polar coordinates. The coordinates of the difference vector in a reference frame where the mid-range vector is located at the North Pole can be expressed as (θ). l φ l ), where θ l This is called the polar coordinate "difference angle". The remaining angles defining the directions of illumination and observation are the azimuth angles φ. h and φ l For details, please refer to [Rus98], the contents of which are incorporated herein by reference in their entirety.

[0140] In some embodiments of this disclosure, barycentric coordinates are used to determine texture properties associated with an appearance model. "Barycentric coordinates" are coordinates in a coordinate system where the position of a point on a simplex (i.e., a triangle in the case of two dimensions) is specified by weights assigned to its vertices. In two dimensions, a simplex is a triangle. The two-dimensional barycentric coordinates of point r relative to vertices r1, r2, and r3 are given by three numbers λ1, λ2, and λ3, such that...

[0141] (λ1+λ2+λ3)r=λ1r1+λ2r2+λ3r3,

[0142] In this context, at least one of the numbers λ1, λ2, and λ3 must be non-zero. It may be necessary that the barycentric coordinates are non-negative (i.e., that the point may be located within the convex hull of a vertex). It may also be necessary that the barycentric coordinates satisfy the condition λ1 + λ2 + λ3 = 1. These are then referred to as "absolute barycentric coordinates".

[0143] An "appearance capture device" is a device capable of determining one or more appearance attributes of an object. Depending on the appearance attribute to be determined, the appearance capture device may take the form of, for example, a camera, a colorimeter, a spectrophotometer, or an imaging spectrophotometer.

[0144] A spectrophotometer is a device used to determine the reflectance and / or transmittance properties (as a function of wavelength, i.e., the spectral response of the object) of a surface or material when illuminated with visible light. Different types of spectrophotometers are known, each with a different measurement geometry and optimized for different purposes. One important type is the integrating sphere spectrophotometer. An integrating sphere spectrophotometer comprises an integrating sphere, a hollow spherical cavity defined by a diffuse white inner surface, having at least one inlet port for illumination and at least one outlet port for observation. The integrating sphere produces a uniform scattering or diffusion effect. Through multiple scattering and reflection, light incident on any point on the inner surface is uniformly distributed to all other points. The influence of the original direction of light is minimized. Examples of integrating sphere spectrophotometers are X-Rite models Ci7860 and Ci7500. Other types of spectrophotometers determine only spectral information for a single narrow range of illumination direction (e.g., 45° to the surface normal) and a single narrow range of observation direction (e.g., 0° to the surface normal). Examples include models 962 and 964, available from X-Rite. Several other spectrophotometers, referred to as "gonometric spectrophotometers" or "multi-angle spectrophotometers," are capable of determining spectral information for multiple combinations of different illumination and observation directions. "Measurement geometry" refers to a specific combination of illumination and observation angles. "Imaging spectrophotometers" additionally possess imaging capabilities, meaning they can include one or more cameras to capture one or more digital images of an object. Examples of multi-angle spectrophotometers with imaging capabilities include the benchtop model TAC7 or the handheld models MA-T6 or MA-T12, available from X-Rite.

[0145] Materials can be transparent, translucent, or opaque. A material is "transparent" if it allows light to pass through without significant absorption and scattering. A material is "translucent" if it allows light to pass through, but the light may be scattered at either of the two interfaces or internally. A material is "opaque" if it does not transmit light. A material may be opaque only in some spectral regions, while being translucent or transparent in others, and vice versa. For example, a material may strongly absorb red light (i.e., it is essentially opaque to red light) and only weakly absorb blue light (i.e., it is transparent to blue light). Some more complex materials, especially those with variable angular appearance, may include combinations of transparent, translucent, and opaque materials. For example, a paint coating may include an opaque base layer and a transparent topcoat. Opaque (reflective or interfering flakes) or translucent pigments may be included in opaque, transparent, or translucent layers of a paint coating.

[0146] For the purposes of this disclosure, a material will be broadly considered "translucent" if a relatively thin slice transmits a considerable portion of the incident radiant flux in at least a portion of the visible spectrum, for example, if a slice with a thickness of 0.1 mm transmits at least 1% of the incident radiant flux in at least a portion of the visible spectrum. In this sense, the term "translucent" includes the term "transparent," that is, for the purposes of this disclosure, transparent materials are also considered translucent. Examples of translucent materials in this sense include many common polymer-based plastic materials, including but not limited to organic polymers (such as PET, PP, PE, PMMA, PS, PC, PVC, PTFE, nylon), organic copolymers (such as styrene-butadiene copolymers), inorganic polymers (such as polysiloxanes), and many natural polymers. Translucent plastic materials may contain pigments and additives. However, for the purposes of this disclosure, other classes of materials may also be translucent, including, for example, silicate glass or paper.

[0147] For the purposes of this disclosure, a material is to be understood as “homogeneous” if its subsurface light transport properties remain unchanged at macroscopic or mesoscopic scales, for example, at scales greater than 1 μm. In particular, homogeneous materials do not include objects with varying appearances at mesoscopic or macroscopic angles, such as thin sheets.

[0148] The term "macroscopic surface geometry" should be understood to refer to the overall geometry of a product, excluding micro or mesoscopic surface structures, i.e., variations in surface geometry at micro or mesoscopic scales below, for example, 1 mm. For example, a local variation of surface height with a local average of less than, for example, 1 mm can be considered a micro or mesoscopic surface structure, and therefore the macroscopic surface geometry can be equivalent to the surface geometry averaged over a length scale of at least 1 mm. In mathematical terms, if the surface geometry at least approximately and at least locally corresponds to a two-dimensional differentiable manifold in three-dimensional Euclidean space, then the surface geometry is "continuously curved."

[0149] The term "rendering" refers to the automated process of generating a photorealistic image of a scene using a computer program. In this disclosure, the scene includes at least one virtual object. Input information for the rendering operation includes a 3D geometric model of at least one virtual object, at least one set of appearance attributes associated with the virtual object, information about the position and orientation of at least one virtual object in the scene, lighting conditions (which may take the form of an environment map), and parameters characterizing the observer, such as viewpoint, focal length, field of view, depth of field, aspect ratio, and / or spectral sensitivity. The output of the rendering operation is an image of the scene, which includes at least a portion of the virtual object. Many different rendering algorithms are known at varying levels of complexity, and software used for rendering can employ a variety of different techniques to obtain the final image. Tracking every single light particle in a scene is generally impractical due to the excessive computational time required. Therefore, simplified techniques for modeling light transport, such as ray tracing and path tracing, are typically used.

[0150] The term "visualization" encompasses rendering and displaying a scene that includes virtual objects. For this display, a display device is used. The term "display device," or simply "monitor," is to be understood as referring to a computer output device used to present information in a visual form. A display device can take the form of a computer monitor, TV screen, projector, VR headset, or the screen of a handheld device such as a smartphone or tablet. A display device can be a touchscreen. In some embodiments, the display device can be a "virtual lightbooth" as disclosed in EP 3 163 358 A1 to provide a particularly realistic impression of the rendered scene.

[0151] The term "database" refers to a collection of organized data that can be accessed electronically by a computer system. In a simple embodiment, a database can be a searchable electronic file in any format. Examples include Microsoft Excel. TM A spreadsheet or a searchable PDF document. In more complex embodiments, the database can be a relational database maintained by a relational database management system using a language such as SQL.

[0152] The term "computer" or "computing device" refers to any device that can be instructed by a program to automatically perform a sequence of arithmetic or logical operations. Without limitation, a computer can take the form of a desktop computer, laptop computer, tablet computer, smartphone, programmable digital signal processor, etc. A computer typically includes at least one processor and at least one memory device. A computer can be a subunit of another device (such as an appearance capture device). A computer can be configured to establish a wired or wireless connection to another computer, including a computer used for querying databases. A computer can be configured to be coupled to a data input device (such as a keyboard or computer mouse) or a data output device (such as a monitor or printer) via a wired or wireless connection.

[0153] The term "computer system" should be broadly understood to include one or more computers. If a computer system includes more than one computer, these computers do not necessarily need to be located in the same place. Computers within a computer system can communicate with each other via wired or wireless connections.

[0154] A processor is an electronic circuit that performs operations on external data sources, particularly memory devices.

[0155] A "memory device," or simply "memory," is a means for storing information for use by a processor. Memory devices can include volatile memory, such as random access memory (RAM), and non-volatile memory, such as read-only memory (ROM). In some embodiments, a memory device can include a non-volatile semiconductor memory device, such as (E)EPROM or flash memory, which may take the form of, for example, a memory card or a solid-state drive. In some embodiments, a memory device can include a high-capacity storage device with mechanical components, such as a hard disk. A memory device can store programs for execution by a processor. Non-volatile memory devices can also be referred to as non-volatile computer-readable media.

[0156] A program is a collection of instructions that can be executed by a processor to perform a specific task.

[0157] A "wired connection" is a connection via an electrical conductor. A wired connection may include one or more cables. A "wireless connection" is a connection involving the electromagnetic transmission of information between two or more points that are not connected by an electrical conductor. Wireless connections include those via WiFi. TM ,Bluetooth TM Connections to 3G / 4G / 5G mobile networks, optical communication, infrared, etc.

[0158] Exemplary embodiments of the method using color matching software

[0159] Figure 1An exemplary embodiment of a method for visualizing the appearance of two or more materials in a vehicle repair context is illustrated.

[0160] Suppose a damaged vehicle needs repair at a body shop. Technicians at the body shop use a handheld appearance capture device 52 to determine a set of appearance properties 54 of the paint coating on an intact vehicle part. The paint coating is an example of a target material, and the intact vehicle part is an example of a target object containing the target material. The appearance properties 54 of the paint coating include, on the one hand, color properties in the form of spectral data for multiple pairs of illumination and viewing directions, and on the other hand, texture properties in the form of image data for multiple pairs (potentially different pairs) of illumination and viewing directions.

[0161] The appearance capture device 52 transmits the measured appearance attributes 54 to a computer system. The computer system may include a local client computer located at the body repair shop. The computer system may further include one or more remote computers located at one or more locations different from the body repair shop. The local client computer may be, for example, a mobile electronic device, such as a laptop or tablet. The remote computer may act as a server for the local client computer at the body repair shop.

[0162] Several elements of the computer system execute software. In a first aspect, the computer system executes model generation software 102. The model generation software 102 generates a first instance 56 of a selected form of appearance model based on measured appearance attributes 54, the first instance 56 representing the appearance of the target material. The first instance of the appearance model is stored in a first AxF file in a form independent of the device and platform.

[0163] In the second aspect, the computer system executes color formulation software 104. Color formulation software 104 determines one or more candidate formulations 60 from a database of reference formulations. Each candidate formulation defines a candidate material (in this example, a candidate paint coating) whose appearance properties may match the measured appearance properties 54 of the target material. Candidate materials include one or more colorants in the base formulation. To determine candidate formulations, formulation software 104 retrieves predetermined appearance properties associated with different reference materials from database 106. Specifically, predetermined appearance properties may be associated with individual colorants and / or with reference formulations containing one or more colorants dispersed in the base formulation. Specifically, database 106 may include two sub-databases: a colorant database storing appearance properties associated with individual colorants and base materials, and a formulation database storing appearance properties associated with reference formulations. The appearance properties in database 106 may have been predetermined by performing measurements on reference objects, including reference materials made according to reference formulations or components comprising reference formulations.

[0164] For example, to determine appearance properties associated with individual colorants in a colorant database, a reduction in the coating of a formulation containing individual colorants at different concentrations can be prepared and measured; to determine appearance properties in a formulation database, a reduction in the coating of a formulation prepared according to a reference formulation can be measured. An appearance capturing device can be used to perform the measurement, which can be of the same type as appearance capturing device 52, or it can be of a different type, as will be discussed in more detail below.

[0165] The formulation software uses predetermined appearance attributes retrieved from database 106 to calculate candidate formulations whose associated appearance attributes are expected to "closely approximate" the measured appearance attributes of the target material. If the appearance difference between the candidate formulation's appearance attributes and the measured appearance attributes is small according to a predefined difference norm, then the candidate formulation's appearance attributes are "closely approximate" the measured appearance attributes of the target material. In other words, the formulation software performs a minimization algorithm to determine candidate formulations that are close to the minimum of the difference norm. The formulation software 104 outputs one or more of the candidate formulations 60 thus determined. A technician selects and optionally manually modifies one of the candidate formulations. The formulation software 104 provides a set of candidate appearance attributes 64 for the selected (and optionally modified) candidate formulation 60. In some use cases, the candidate formulation corresponds to a single reference material, i.e., the candidate appearance attributes include appearance attributes associated with a single reference material. In other use cases, the candidate appearance attributes are calculated based on measured appearance attributes associated with multiple reference materials. Reference materials may include individual components of the candidate formulation in a manner that allows determination of the appearance attributes associated with these individual components. This is particularly useful when modifying a candidate formulation by changing the proportions of individual components. The model generation software 102 receives the candidate appearance attributes 64 and generates a second instance 66 of the appearance model, representing the expected appearance of the paint coating prepared according to the candidate formulation 60. The second instance 66 of the appearance model is stored in a second AxF file.

[0166] In the third aspect, the computer system executes rendering software 108. The rendering software renders a virtual object 72, that is, it creates a photorealistic digital image of the virtual object 72 based on a geometric model of the surface geometry of the virtual object 72, at least one instance of the appearance model, and lighting and viewing conditions. The rendered virtual object 72 is displayed in a scene on a display 70. The geometric model defines a continuous three-dimensional macroscopic surface geometry having surface normals distributed over relatively large solid angles; that is, it includes curved or straight surface portions having directions perpendicular to the surface portions pointing in many different directions. In 3D computer graphics, polygon modeling is well known as a method for modeling objects by using polygon meshes to represent or approximate their surfaces. These are also considered continuously curved three-dimensional macroscopic surfaces if the polygon meshes substantially appear as continuously curved surfaces during rendering. The rendering software generates a two-dimensional image of the virtual object 72 in a specific orientation and under specific lighting conditions (assuming specific viewing conditions). The surface geometry, orientation, and illumination conditions are selected in such a way that the rendered image gives a good impression of the appearance of the virtual object with respect to a wide range of angles between the surface normal, the illumination direction, and the viewing direction, so as to allow the observer to evaluate the appearance of the rendered virtual object 72 with respect to a wide range of these directions.

[0167] Virtual object 72 has first and second parts 72a and 72b that are adjacent to each other. The first part 72a is rendered using a first instance 56 of the appearance model, while the second part 72b is rendered using a second instance 66 of the appearance model. A virtual dividing line 74 is visualized between the first and second parts 72a and 72b. The first and second parts 72a and 72b appear to meet at the virtual dividing line 74.

[0168] Display 70 can be located at the auto repair shop. For example, display 70 could be a touchscreen display of a local client computer at the auto repair shop. A technician at the auto repair shop can use their finger or a pointing device such as a digital pen, trackpad, or mouse to move a virtual line 74 across display 70 and observe how the appearance matches or differs between first and second instances of an appearance model rendered on a virtual object 72; that is, how the appearance matches or differs between the measured appearance of the actual paint coating on the vehicle and the expected appearance of a paint coating prepared according to a candidate formulation. Optionally, the technician can change the shape of the virtual object, rotate the virtual object in space, and simultaneously change the illumination and viewing angles. Optionally, the technician can change the illumination conditions, including the direction of illumination and the selection of the light source.

[0169] In some embodiments, the virtual object 72 may have the shape of an actual part of the damaged vehicle, such as the shape of the target object 50. For this purpose, the rendering software 108 can retrieve a three-dimensional geometric model corresponding to the geometry of the vehicle part from a suitable memory (e.g., from a database 110 storing geometric data of multiple vehicle parts). For this purpose, a technician can provide, for example, the manufacturer and model of the damaged vehicle or other vehicle information such as a unique vehicle identification number, and one or more components to be rendered as the virtual object 72.

[0170] In other embodiments, the rendered virtual object 72 is a three-dimensional object that differs from the actual car part to be repaired, but has a three-dimensional shape useful for examining the appearance properties of various materials. Also in such embodiments, the rendering software 108 can retrieve the three-dimensional geometric model of the virtual object from memory, for example, from database 110. Regardless of whether the virtual object 72 represents an actual car part, the virtual object 72 preferably allows for simultaneous examination of color and texture differences at a large number of angles between surface normals, illumination directions, and viewing directions.

[0171] When viewed on monitor 70, the appearance of the first part 72a of the rendered virtual object does not need to perfectly match the appearance of the actual target object 50. In particular, monitor 70 does not need to be calibrated. It is important that the appearances of the first and second parts 72a and 72b are directly comparable to each other in both color and texture. This is possible even if the colors on the monitor are not true colors. By using the same appearance model for both parts, the direct comparability of the appearances of the two parts 72a and 72b is ensured.

[0172] It should also be noted that the appearance model itself and the rendering software do not need to be perfect to ensure direct comparability. The appearance model and rendering software are sufficient to make a reasonably realistic judgment on the differences between two instances representing the actual coating material on the target object 50 and the coating material according to the candidate formulation 60.

[0173] If a visual comparison of the first and second parts 72a, 72b shows an unsatisfactory match, the technician can modify candidate recipe 60 in formulation software 104 by selecting a different candidate recipe or by modifying a previously selected candidate recipe. Formulation software 104 provides the appearance attributes of the modified candidate recipe to model generation software 102, which creates a second instance of the modified appearance model based on these attributes. Rendering software 108 can be instructed to render the second part of the virtual object using the modified second instance of the appearance model, thereby replacing the previous candidate recipe in the visualization with the modified recipe, or the rendering software can be instructed to divide the virtual object into three parts 72a, 72b, 72c to visualize the reference material along with the two candidate recipes, thereby adding another movable dividing line 76 (“recursive divider control”) to the visualization.

[0174] Once a satisfactory match has been achieved, an actual test object 80 can be produced using the selected candidate formulation 60, for example, a reduced version. The appearance attributes of the test object 80 can be determined using the appearance capture device 82. The appearance attributes of the test object 80 can be compared with those of the target object 50 to determine whether the match is objectively within tolerance. This can be done by evaluating an appropriate difference norm between the measured appearance attributes of the test object 80 and those of the target object 50. Additionally or alternatively, a third instance of the appearance model can be generated using the appearance attributes of the test object 80, and the target material and the test material can be visualized side-by-side on the display 70 using the associated instance of the appearance model. Of course, if both the physical test object 80 and the physical target object 50 are located in the same location, a direct comparison of the two objects is also possible. If the match is still not within tolerance, the formulation software 104 can further modify the candidate formulation 60 by considering the differences between the predicted appearance attributes of the candidate formulation 60 and those appearance attributes already actually determined for the test object 80.

[0175] Test subject 80 can be manufactured by technicians in a body repair shop. However, most often, test subject 80 will be manufactured by a paint supplier located far from the body repair shop. Therefore, the appearance capture device 82 used to determine the appearance attributes of test subject 80 may differ from the appearance capture device 52 used to determine the appearance attributes of target object 50.

[0176] In the above embodiment, it is assumed that a technician at the auto repair shop not only operates the appearance capture device 52 to determine a set of appearance attributes 54 of the target object 50, but also operates the formulation software 104 to define one or more candidate formulations and uses the display 70 to compare the first and second parts 72a, 72b of the virtual object 72. However, these tasks can also be distributed among different people working at different locations. For example, the technician at the auto repair shop can transmit the appearance attributes 54 to a remote paint supplier, and a paint expert at the paint supplier can use the formulation software 104 to define candidate formulations and compare different parts of the virtual object to determine whether the expected match is satisfactory. Therefore, the computer system can include at least one computer under the control of the paint supplier, such as a computer executing the formulation software 104, model generation software 102, and rendering software 108. Many other possibilities exist for the division of tasks among the different entities involved in the process. Therefore, it will be apparent to the technician that the computer system can include several computers that may be located in different locations and may be under the control of different entities, communicating via wireless or wired data communication connections.

[0177] Other possible ways to visualize objects using instances of appearance models

[0178] In alternative embodiments, instances of the appearance model can be used to visualize scenes other than those comprising only a single virtual object, which has two or more parts visualized using different instances of the appearance model. For example, a scene comprising two or more identical or different virtual objects can be visualized, each of which is visualized using a different instance of the appearance model. Generally, a scene comprises one or more virtual objects, and different parts of the scene are visualized using different instances of the appearance model. For example, two objects of the same shape (e.g., two vehicle parts of the same shape, such as rearview mirrors) can be visualized side-by-side using first and second instances of the appearance model combined with a common geometry model.

[0179] flow chart

[0180] Figure 2An exemplary flowchart illustrating a method for visualizing the appearance of two materials is shown. In step 501, a user (e.g., a technician at a car body repair shop) determines the appearance attributes 54 of a target object 50 by measuring the light reflection and / or transmission properties of the target object, and optionally texture properties, using a first appearance capture device 52. In step 502, the appearance capture device 52 transmits these measured appearance attributes to a component of a computer system, such as a handheld computer in the car body repair shop. In step 503, the computer system uses model generation software 102 to generate a first instance 56 of the appearance model from the measured appearance attributes. In step 504, the computer system reads the appearance attributes of one or more reference materials from a database 106. In step 505, the computer system uses formulation software 104 to determine one or more candidate formulations 60. The formulation software 104 may perform step 505 by simply selecting one or more reference materials having appearance attributes similar to the measured appearance attributes 54 of the target object 50, by modifying the formulation of the retrieved reference materials, or by generating a new candidate formulation 60 if no close match is found in the database 106. In step 506, the computer system uses formulation software 104 to determine the appearance attribute 64 associated with candidate formulation 60. In step 507, the computer system uses model generation software 102 to generate a second instance 66 of the appearance model from the appearance attribute 64 associated with candidate formulation 60. In step 508, the computer system obtains the geometric model of the virtual object. In step 510, the computer system uses rendering software 108 to visualize the virtual object 72 based on the two instances 56 and 66 of the appearance model, the geometric model of the virtual object 72, and the lighting and viewing conditions. In step 509, the user compares the rendered virtual object portions 72a and 72b to obtain an acceptable appearance match. In step 511, if the visualized virtual object portions 72a and 72b do not provide an acceptable visual match, the user can modify the formulation or select a different candidate formulation 60. Once an acceptable rendering match is obtained, in step 512, the user prepares a test object 80 using the candidate formulation. In step 513, the same or different users use a second appearance capture device 82 to determine the appearance attributes of the test object 80. In step 514, the appearance attributes of the test subject 80 are transmitted to a computer system, and the computer system uses formulation software 104 to determine a modified formulation, if necessary, to refine the match. Steps 506 to 514 can then be repeated for the modified formulation. In some embodiments, the measured appearance attributes are compared with the expected appearance attributes to determine the level of appearance information, and as referenced Figure 14-16The appearance attributes are edited as described in the discussion. In some embodiments, first and second appearance models are compared to determine the level of appearance information, and the appearance attributes are edited. In some embodiments, the measured appearance attributes are compared with a second instance of the appearance model to determine the level of appearance information, and as referenced... Figure 14-16 As discussed in the previous discussion, an edited appearance model was obtained.

[0181] Computer System: Exemplary Hardware

[0182] Figure 3 The diagram shows that it can be used Figure 1 and 2 An exemplary hardware-oriented block diagram of the computer system used in the method illustrated herein. In this example, the computer system includes two main components: a local client computer (e.g., a laptop or tablet computer) 300, which may be located at a vehicle repair shop, and a remote server computer 360, which may be located at a paint supplier's premises.

[0183] As is well known in the art, various components of the local client computer 300 communicate with each other via one or more buses 301. The local client computer 300 includes one or more processors 310. As is well known in the art, processors 310 may include, for example, a single-core or multi-core CPU and a GPU. The local client computer 300 also includes one or more non-volatile memory devices 320, such as flash memory devices and / or hard disk drives. The non-volatile memory 320 stores, in particular, the operating system 321 of the local client computer 300 and several applications, including model generation software 102 and rendering software 108. The non-volatile memory 320 also stores user data. The local client computer 300 also includes random access memory (RAM) 330, and input / output (I / O) interface 340 and communication interface 350. Attached to the communication interface are a display device 70 and a pointing device 90. The communication interface 350 may include, for example, an Ethernet interface, a WiFi interface, or Bluetooth. TM One or more of the following: interfaces, etc. A communication interface can be used to communicate with a remote 360 ​​server.

[0184] The remote server computer 360 can be configured similarly to the client computer 300. It stores configuration software 104 for execution. The client computer 360 may include or be connected to the database 106. Communication between the server computer 360 and the client computer 300 can be conducted via wired or wireless networks, such as via LAN or WAN, and particularly via the Internet.

[0185] The client computer also communicates with the first and / or second appearance capture devices 52, 82 via communication interface 350.

[0186] In other embodiments, some functions of the client computer 300 are instead transferred to the server computer 360. Specifically, the server computer (rather than the client computer) can execute the model generation software 102 and / or rendering software 108. This can be useful if the client computer is a "thin" client with limited computing power.

[0187] In some other embodiments, the computer system consists of only a single computer that executes all of the components described above in the software components.

[0188] Exemplary appearance capture device

[0189] In the example above, appearance capture devices 52 and 82 are preferably multi-angle spectrophotometers with imaging capabilities. Such devices are known in themselves. For example, the MA-TX series available from X-Rite can be used.

[0190] Figures 4 and 5 illustrate exemplary handheld appearance capture devices that can be used in conjunction with the present invention. The appearance capture devices of Figures 4 and 5 are described in more detail in document US20140152990A1, the contents of which are incorporated herein by reference in their entirety for the purpose of teaching handheld appearance capture devices.

[0191] An appearance capturing device is configured to capture the visual impression of a measured object. Hereinafter, the appearance capturing device is also referred to as a “measuring device” or simply “device.” Hereinafter, the term “measuring array” is understood to refer to the sum of the components of a handheld measuring device used to illuminate measuring points on the surface of the measured object, capture the light reflected from those measuring points, and convert it into a corresponding electrical signal. The term “device normal” is understood to refer to an imaginary straight line that is fixed relative to the device and extends substantially through the center point of the measuring opening of the measuring device, and is perpendicular to the surface of the measured object when the measuring device is positioned on the planar surface of the measured object. The plane of the measuring opening is generally parallel to the surface of the measured object, such that the device normal is also perpendicular to the measuring opening. The term “vertical” is understood to refer to the direction of the device normal. Therefore, a vertical section is understood to refer to a planar section in a plane containing or parallel to the device normal. In the following description of the measuring device, the direction and / or angle are relative to the device normal, which is spatially fixed relative to the measuring device.

[0192] The handheld measuring device shown in Figure 4 is indicated as a whole by reference numeral HMD. It includes a housing H housing the measuring array, as shown in Figure 4, and an electronic control array (not shown) for controlling the measuring array. Two gripping parts 1 and 2 are implemented laterally on the housing H. A wrist strap 3 is arranged on the upper side of the housing H. A display array 4 is provided on the front side of the housing H. An operating member (not shown) is arranged on the upper side of the housing H.

[0193] The lower side of the housing H includes a housing base 5 reinforced by a substrate 7, which is equipped with a measurement opening 6. The housing base 5 includes a hole (not indicated by a reference mark) in the area of ​​the measurement opening 6, allowing light to exit the interior of the housing through the hole and the measurement opening 6, and conversely, allowing light from the outside to enter the interior of the housing through the measurement opening 6 and the hole. Three support members 7a, 7b, and 7c are arranged around the measurement opening 6 on the substrate 7 and help to ensure proper positioning of the measuring device even on curved measuring surfaces, such that the device normal is completely or at least largely coincident with the normal of the measuring surface at the center point of the measurement point.

[0194] The device normal is indicated by reference mark DN in Figure 4. It is perpendicular to the substrate 7 and extends through the center point of the measuring opening 6.

[0195] The arrangement of the measurement array is illustrated in Figure 5. It includes an arcuate body 10, which is fixedly held within a housing H, and the optical and / or optoelectronic components of the measurement array are arranged within the arcuate body 10. In the exemplary embodiment shown, these components include seven illumination devices 21, 22, 23, 24, 25, 26, and 27, and three pick-up devices 31, 32, and 33. Furthermore, an illumination device 28 for diffuse illumination is provided adjacent to the measurement opening 6.

[0196] Seven irradiation devices 21 to 27 irradiate measurement points on the surface of the object being measured along different irradiation directions relative to the device normal DN. For example, the optical axes of the irradiation devices 21 to 27 can be oriented at angles of -60°, -45°, -30°, -20°, 0°, +30°, and +65° relative to the device normal. All seven irradiation devices 21 to 27 are arranged such that their optical axes lie in a common plane containing the device normal DN, which is referred to below as the system plane SP.

[0197] Two of the three pickup devices 31 to 33 are implemented as spectral measurement channels; the third pickup device is implemented as a spatially resolved color measurement channel. The pickup devices receive measurement light reflected from the region of the irradiated measurement point of the object being measured at observation angles of +15° and +45° in the system plane SP. The two pickup devices 31 and 32 forming the spectral measurement channel include two spectrometers 31a and 32a, to which the measurement light is fed via lenses and optical fibers 31c and 32c. The pickup device 33 forming the spatially resolved measurement channel includes a color-enabled (RGB) camera 33a to which the measurement light is applied via a beam splitter and a lens (not shown). The beam splitter is located in the beam path of pickup device 32 and laterally guides a portion of the measurement light from the arcuate body 10 onto camera 33a at an observation angle of +15°. Therefore, pickup devices 32 and 33 share the measurement light and receive it at exactly the same observation angle.

[0198] The measurement geometry is the opposite of ASTM standards E2194 and E2539, which define two specular illuminations at 15° and 45° and six specular spectral channels at 0°, 30°, 65°, -20°, -30°, and -60° for measuring metallic and pearlescent pigments. An additional illumination device 22 at a -45° angle is provided for use in conjunction with the pickup device 31 to measure gloss.

[0199] Two spectrometers, 31a and 32a, perform spectral analysis on the measurement light fed to them at observation angles of 45° and 15°, respectively, and each measurement produces a set of spectral measurements, each corresponding to an intensity within a different wavelength range. Spectrometers 31a and 32a do not perform spatial analysis on the measurement light; that is, they perform spectral analysis on the entire measurement light they receive.

[0200] The RGB camera 33a resolves both spatially and spectrally the measurement light fed to it at a 15° viewing angle. Based on the three colors RGB, the spectral resolution is limited to three channels. Each measurement by the RGB camera correspondingly produces a raw dataset of 3*n measurements, where n is the number of resolvable pixels.

[0201] An illumination device 28 provides diffuse illumination, enabling the measuring device to also support measurement modes with diffuse illumination conditions. The illumination device 28 is configured for LED background illumination, directly illuminating the object being measured from a large spatial angle. It includes two rows of white light-emitting diodes arranged on both sides of the measuring opening 6 and two inclined diffuse films, each assigned to one row, for homogenizing the illumination. The two rows of LEDs can be separately controlled by a control array.

[0202] Of course, depending on the intended application, other types of irradiation devices may also be used.

[0203] The aforementioned handheld measuring device is equipped with seven illumination devices and three pickup devices for measurement purposes. Other combinations of illumination devices and pickup devices are possible. The illumination devices do not necessarily need to be arranged in a plane.

[0204] The output of the measuring device includes a set of appearance attributes. Some of these appearance attributes are color attributes, such as RGB, CIELAB, or CIEXYZ values, either in the form of spectral measurements determined by a spectrometer or color values ​​derived from it. Other appearance attributes are texture attributes, such as texture data, either in the form of raw image data obtained from an RGB camera or in the form of image data derived from it, such as monochrome grayscale image data. This set of appearance attributes obtained from the measuring device is typically sparse compared to the appearance attributes required to generate an instance of the appearance model, as will be discussed in more detail in the next section.

[0205] Example of appearance model

[0206] As outlined above, many different appearance models have been proposed in the art. An example, particularly useful for describing the appearance of coatings on modern vehicles, will be described below. This exemplary appearance model is an improvement upon the model proposed in [RMS+08]. The contents of [RMS+08] are incorporated herein by reference in their entirety for the purpose of teaching the exemplary appearance model. The appearance model currently discussed is also referred to as the "CPA2 model".

[0207] The appearance model assumes the coating consists of two outermost layers: an opaque or translucent color layer, which is covered by a smooth, transparent coating. The color layer may contain highly reflective particles (so-called "flakes"). Incident light with direction i travels along its "refracted" incident direction. The light is refracted into the transparent coating before reaching the color layer. The reflected light travels along the outgoing direction before being refracted into the surrounding air in one direction (o). Propagation occurs through the transparent coating. Refraction is modeled using standard geometric optics, assuming the typical refractive index of the transparent coating.

[0208] The appearance model of the color layer is divided into three parts:

[0209] (a) Monochromatic luminance BRFD component, which models the spatial and wavelength-averaged overall reflection behavior (luminance variation) of the material. The luminance BRFD component can represent high frequencies in the angular domain.

[0210] (b) The spectral portion in the form of a discrete color table, which describes the low-frequency angular variations (color shifts) of the color.

[0211] (c) A BTF portion in the form of a texture table, which captures the effect of reflective flakes in the color layer. The BTF portion is represented by multiple fixed-size images, each representing the texture under a specific combination of illumination and viewing angles.

[0212] These three parts are combined into a BRDF function representing the spatial variation of the appearance of the painted surface:

[0213]

[0214] here,

[0215] i represents the direction of incident light in the air.

[0216] o represents the direction of reflected light in the air.

[0217] x is the position on the surface of the object.

[0218] n is the surface normal at position x.

[0219] The direction of (refraction) of incident light in the transparent coating.

[0220] The direction of (refractive) light reflected in the transparent coating.

[0221] F(n, d, η) is a function approximating Fresnel reflection at the boundary of a medium with a relative refractive index η (or the Fresnel formula itself).

[0222] For the monochrome BRDF model from (a),

[0223] For the interpolated color table from (b),

[0224] For the texture table of interpolation from (c),

[0225] δ(d1, d2) is the Dirac delta function on O(3), which is nonzero only when d1 = d2, and

[0226] r(n, d) is the direction of reflection d at the surface where the normal n is located.

[0227] (a) Example of the luminance BRDF section

[0228] A BRDF model based on multi-lobed micro-facets is used to model the monochromatic luminance BRDF component:

[0229]

[0230] here,

[0231] for and The direction along the middle (also known as the "middle vector")

[0232] a is the diffuse albedo.

[0233] K represents the number of BRDF lobes.

[0234] s k Let be the multiplier of the k-th lobe.

[0235] D is the normal distribution function of the micro-surface elements according to the selected model.

[0236] α k Let be the roughness parameter of the k-th lobe.

[0237] F 0，k Let be the Fresnel function parameter of the k-th lobe, which may be the refractive index or the exponent of reflection at perpendicular incidence.

[0238] G is the so-called geometric structure term based on the selected model.

[0239] In some embodiments, a Cook-Torrance BRDF model with a Schlicker-Fresnel approximation is used. Here, D, F, and G are defined as follows:

[0240]

[0241] F(n, d, F) 0，k ) = F 0，k +(1-F 0，k (1-)<n,d> ) 5

[0242]

[0243] here,

[0244] θ h Represents the mid-way vector in a fixed object reference frame. The polar angle, where the surface normal n defines the z-axis (“North Pole”).

[0245] These represent the vectors in the reference frame, respectively. and The z-component.

[0246] The free parameters of this model are a and s. k α k and F 0，k (For k = [1..K]). Typically, K is chosen to be between 1 and 3.

[0247] (b) Examples of the spectral section

[0248] The spectral portion is modeled using a discrete color table, which comprises values ​​of one or more color attributes (e.g., hue and saturation) for combinations of multiple directions. The values ​​of these color attributes will be simply referred to as "color values" below. Advantageously, the Rusinkiewicz parameterization [Rus98] is used to indicate the directions. In a simplified embodiment, the color table is bivariate, meaning the values ​​of the color attributes depend only on the polar angles (half-angles and difference angles) in the Rusinkiewicz parameterization, as will be explained below. These polar angles will also be referred to below as the "coordinates" of the color table.

[0249] Figure 6 An exemplary representation of a bivariate discrete color table is shown. On the horizontal axis, the half-angle θ h That is, the mid-way vector The polar angle is used as the first coordinate. The half-angle is also called the "flake angle" because it defines the angle between the surface normal n of the paint layer and the normal of the flake in the paint layer, which will cause incident light to change from the incident direction. Reflected in the direction of emission In the middle. On the vertical axis, the midway vector. and incident direction The range angle θ between l Used as the second coordinate. Range angle θ l Incident direction in the transformed reference frame The polar angle, in the transformed reference frame, the mid-way vector Define the z-axis. It can be interpreted as indicating the direction of incidence relative to the slice normal. The color table contains each pair of coordinates (i.e., the polar angle θ). h and θ l Each combination of coordinates represents one or more color values. The color table is defined for each pair of coordinates containing one or more color values. Figure 6 The circle in the diagram represents such a pair of coordinates (polar angle θ). h and θ l The combination of these (positions) will be referred to as “positions” in the color table below. Positions in the color table are spaced regularly. Therefore, the color table is represented by a regular rectangular grid of positions and their associated color values. The positions of two exemplary entries in the color table are labeled “a” and “b”.

[0250] The appearance model assumes that the paint material has isotropic reflection. For isotropic materials, color does not depend on the mid-range vector in a fixed object reference frame. azimuth φ h Therefore, the color table does not consider the azimuth angle φ. h .

[0251] Furthermore, the model assumes that the effect particles (“plates”) themselves are isotropically reflective, and that the color shift is dominated by the specular reflection component of the reflected light. Therefore, the color depends only slightly on the incident direction in the transformed reference frame. azimuth φ I Therefore, the color chart does not consider the azimuth angle φ. I .

[0252] exist Figure 6 In the color table, at θ h and θ l The blank areas at larger values ​​indicate combinations of "forbidden" angles, which are physically impossible due to refraction and total reflection at the interface between the transparent coating and the surrounding air. The color chart does not include any color values ​​for these combinations of angles.

[0253] (c) Examples of BTF sections

[0254] The appearance model includes a bidirectional texture function (BTF) model. Generally, a BTF model is a multidimensional function dependent on the planar texture coordinates (x, y) and the viewing and illumination spherical angles. The BTF portion is modeled using a discrete texture table. Each entry in the texture table is a texture slice, representing the effect of a slice for a specific combination of illumination and viewing directions. In a simplified embodiment, Rusinkiewicz parameterization is also used for the texture table, and the texture table is again bivariate, again determined by the angle θ. h and θ l To parameterize.

[0255] Figure 7 A simplified example of a texture slice is shown in the figure. The bright spots in the figure indicate the reflection of the slice, whose slice normal is at the associated slice angle φ. h Orientation, and its position at the associated difference angle φ I It can be seen everywhere.

[0256] Figure 8 The discrete properties of the bivariate texture table are illustrated. In the appearance model, discrete positions are marked by circles (by their coordinates θ). h θ l (Definition) Provides texture slices. Due to the higher storage requirements per texture slice compared to color values ​​in a color table, θ in the texture table... h and θ l The spacing between the discrete values ​​may be larger than that in the color table. Similar to the color table, there are no entries for "forbidden" angles in the texture table. The positions of three exemplary texture slices are labeled a′, b′, and c′.

[0257] Instances of generating appearance models

[0258] The currently proposed methods, based on measurements using handheld appearance capture devices or from formulation software, are limited to only a limited number of directions. A limited set of appearance properties is available for combinations of light. These appearance properties are typically not available for combinations of the same incident and outgoing light directions required by the discrete color and texture tables in the appearance model. Furthermore, the available appearance properties are not necessarily of the same type as those required by the appearance model. For example, the available appearance properties typically do not include the parameters of the monochromatic luminance BRDF model as described above.

[0259] Therefore, to generate instances of the appearance model, the appearance attributes of the appearance model need to be determined from a finite set of available appearance attributes. It should be noted that this set of appearance attributes of the appearance model may have a much larger cardinality than the finite set of available appearance attributes. Therefore, generating instances of the appearance model may involve interpolation and extrapolation.

[0260] As discussed above, the free parameters of the luminance BRDF part are a and s. k α k and F 0，k (For k = [1..K]). These parameters can be determined using nonlinear optimizations known in the art [RMS+08].

[0261] Next, the bivariate color table needs to be populated. The color attributes may only apply to a few pairs of angles θ. h and θ l Available, in Figure 6 The diagram is marked with a cross. The color attribute corresponds to each pair of angles (θ) available to it. h θ l (This will be referred to as a "sample point" below.) A sample point defines the convex hull, which lies on... Figure 6 The area shown is represented by a dashed line. For this, each position in the bivariate color table requires a color value (θ). h θ l The locations will be referred to as "target locations". In this example, target locations are distributed on a regular grid, and therefore these locations can also be called "grid points". The color values ​​at the target locations will be called "target color values". They may have the same format as the available color attributes, or they may have different formats. For example, available color attributes may include spectral data, while color tables may include reduced values ​​(e.g., RGB tri-color values, CIEXYZ tri-color data, or values ​​in another color space such as CIELAB). For the following discussion, it is assumed that the transformation between the format of the available color attributes at the sample points and the format of the color values ​​in the color table is known.

[0262] Color values ​​at target locations can be easily interpolated and extrapolated from available color attributes at sample points using standard interpolation and extrapolation procedures. If the format of color values ​​in the color table differs from the format of available color attributes at sample points, necessary transformations can be applied before or after interpolation / extrapolation, provided that both the available color attributes at sample points and the color values ​​in the color table are expressed in a linear color space. If color values ​​in the color table are not expressed in a linear color space (e.g., as CIELAB values), interpolation / extrapolation is performed first in the linear color space, and then the transformations are applied.

[0263] For example, for interpolation within a convex hull, nearest neighbor interpolation, inverse distance weighting [Sh68], or Gaussian process regression [W98] can be used between color attributes at available sample points or between color values ​​obtained from these color attributes through the transformation, as is well known in the art.

[0264] In a preferred embodiment, for the minimum θ with a value less than the convex hull h θ h or higher than the maximum θ of the convex hull h θ h Extrapolating the target location outside the convex hull can be done using the color attribute (or derived color value) of the nearest neighbor sample point. The nearest neighbor sample point should be understood as having the smallest Euclidean distance from the target location. The sample points. Figure 6 This is illustrated for the target location "a". The color value at the target location "a" is derived from the available color attributes at sample point A, which is the nearest neighbor sample point to the target location "a".

[0265] Extrapolation at all other target locations outside the convex hull, especially with low or high θ. l For extrapolation at those locations, the preferred embodiment will not only use nearest neighbor sample points, but will also interpolate between the color attributes (or derived color values) of the two sample points closest to the target location. Figure 6 This is illustrated by the entry at target location "b". The two closest sample points are points B and C. Therefore, the color value at target location "b" will be determined by interpolation between the color attributes (or derived color values) at sample points B and C.

[0266] Of course, other “well-performing” interpolation and extrapolation methods can also be used, as they are known in the art.

[0267] Finally, the bivariate texture table needs to be populated. Texture samples in the form of images may only be for a few pairs of angles θ. h and θ l(Hereinafter referred to again as “sample points”) are available. For example, as described above, the appearance capture device 52 may have one or more cameras. The available texture samples may be image data for multiple illumination angles and a single viewing angle (e.g., in the case of a single RGB camera) or image datasets for multiple illumination angles and multiple viewing angles (e.g., in the case of multiple RGB cameras).

[0268] exist Figure 8 In the example, only six texture samples are available. The corresponding sample points are in Figure 8 The points are marked with a cross and labeled A′, B′, C′, D′, E′, and F′. ​​The convex hulls of these sample points are again shown as dashed lines. The texture samples at the sample points are called the “source textures.” Texture slices need to be determined to fill the bivariate texture table with respect to the angle θ. h θ l This is again referred to as the "target location" with "target coordinates." Similar to the case of the color table, in this example, these points are arranged on a regular grid and therefore can also be called "grid points." Therefore, the texture slice at the target location is called the "target texture." They can be determined from the available source textures as follows:

[0269] -For each target location (θ) in the texture table h θ l This determines whether the target location is inside the convex hull.

[0270] - If the target location is inside the convex hull, then "Statistical Texture Composition" is used to interpolate the target texture at that location from the surrounding source textures, as explained in more detail below. For example, for Figure 8 Position a′ in the data will be synthesized using statistical texture.

[0271] - If the target location is not within the convex hull, the target texture is defined using the source texture of the nearest neighbor sample point (“First Strategy”), or a constant texture without any spatial variation is used, i.e., an “empty” texture that does not contain any reflections from the slab (“Second Strategy”). In one possible implementation, for a maximum θ smaller than the available sample points... h The value θ h The first strategy can be adopted, and for the maximum θ greater than the available sample points... h θ h Adopt the second strategy. For example, for Figure 8 For position b′, the first strategy would be appropriate, while for position c′, the second strategy could be used. It is also possible to use statistical texture synthesis for target positions outside the convex hull, provided that at least two sample points are sufficiently close to the target position. For example, using the source textures at sample points B′ and D′, statistical texture synthesis for target position b′ might be appropriate.

[0272] Statistical texture synthesis

[0273] Now refer to Figures 9 to 13 To explain Figure 8 An exemplary embodiment of statistical texture synthesis for target position a′.

[0274] Initially, three source textures at different sample points were selected for the target location a′. For this, a Delaunay triangulation was created for all sample points A′ to F′. In two dimensions, as in this example, the result of the Delaunay triangulation is a set of triangles. Figure 8 In the diagram, the sides of the resulting triangle are represented by straight lines from each sample point A′ to F′. Now, select the triangle containing the target location a′ and determine the sample points at the corners of this triangle. In this example, these are sample points B′, D′, and E′. For subsequent processes, the source texture at these three sample points will be used.

[0275] Next, the interpolation weights of the three source textures are determined. To do this, the absolute centroid coordinates of the target position a′ relative to the selected triangle are determined, i.e., the non-negative centroid coordinates λ1, λ2, and λ3 satisfying the condition λ1 + λ2 + λ3 = 1. These centroid coordinates are then used as the interpolation weights of the source textures at the corners of the selected triangle. Figure 8 In the example, the absolute barycentric coordinates relative to sample point B′ are quite large, while the absolute barycentric coordinates relative to sample points D′ and E′ are much smaller. Therefore, the source texture at sample point B′ will receive a relatively large interpolation weight, while the interpolation weights for sample points D′ and E′ will be much smaller. For all other source textures, the interpolation weights are set to zero.

[0276] Now, synthesize the target texture for the target location a′ from the three source textures at sample points B′, D′, and E′. This will be about Figure 9 To explain.

[0277] Therefore, one of three source textures is randomly selected, with the probability proportional to the interpolation weight of the source texture. Figure 9 In the example, the source texture 211 at sample point B′ has been randomly selected.

[0278] From the source texture 211, texture patches are now extracted at random locations within the source texture, i.e., a small portion of the source texture, indicated by small rectangles within the source texture. Figure 9 In the example, texture patch 212 is extracted. The extracted texture patch 212 is now modified such that one of its statistical properties at least approximately matches the corresponding average statistical property. In this example, the extracted texture patch 212 is modified such that its pixel value histogram approximately matches the average pixel value histogram 202. This will now be explained in more detail.

[0279] Figure 10 The diagram illustrates an exemplary pixel value histogram. A pixel value histogram contains multiple relative frequency values, one of which corresponds to each discrete pixel value in the image. For example, if the pixel value ranges from 0 to 255, then for each pixel value between 0 and 255, the pixel value histogram will include the relative frequency of that pixel value in the image. In some embodiments, pixel values ​​can be binned, each bin corresponding to a range of pixel values, and the pixel value histogram can accordingly include a reduced number of relative frequency values, each representing the relative frequency of a pixel value within a bin. Pixel value histograms can be used to evaluate the distribution of brightness in an image, including overall brightness and contrast. For example, Figure 10 The pixel value map will indicate that the image contains some bright spots or areas in front of a relatively dark background.

[0280] exist Figure 9 In the diagram, all pixel value histograms are shown in a very simplified manner, with only five bins for each pixel value histogram. The pixel value histogram of the source texture 211 from which texture patch 212 is extracted is shown as histogram 213. The average pixel value histogram is shown as histogram 202. This average histogram has been obtained as a weighted average of the pixel histograms 213, 222, and 232 of the three source textures 211, 221, and 231 at sample points B′, D′, and E′, respectively, with the weights being the interpolation weights determined above. The pixel values ​​of texture patch 212 are now modified to obtain a pixel value histogram that more closely matches the average pixel value histogram 202. The modification is performed by applying a monotonically non-decreasing pointwise transformation to each pixel value in texture patch 212. Histogram matching algorithms for finding suitable transformations are well known in the field of digital image processing associated with brightness and contrast modifications. The modified texture sheet is illustrated as texture sheet 214, and the resulting pixel value histogram is illustrated as histogram 215. The resulting pixel value histogram 215 now closely matches the average pixel value histogram 202.

[0281] The modified texture patch 214 is now inserted into the target texture 201 so that it seamlessly fits the texture content already present in the target texture 201. For this purpose, the first modified texture patch 214 can simply be placed in a corner of the target texture 201. Each subsequent texture patch is inserted into the target texture using a technique called “MinCut” or “Graphcut”. See reference [Kw03]. In very simple terms, the seam that enhances the visual smoothness between existing pixels and the newly placed patch is calculated. The texture patch and the existing content are stitched together along this seam.

[0282] exist Figure 11The diagram illustrates such an insertion step. In this example, a modified texture piece 214 is inserted into a target texture 201 that already includes texture piece 203. The position 204 of the newly inserted piece 214 is selected such that it overlaps with the existing piece 203. A seam 205 is calculated in the overlapping area such that the existing pixels and the newly inserted pixels along the seam appear visually smooth, i.e., no visible boundary is created along the seam, as explained in detail in [Kw03]. The existing piece 203 and the newly inserted piece 214 are stitched together along this seam, with the remainder of each piece discarded. This results in a larger piece 206.

[0283] This process is now repeated frequently as needed to completely fill the target texture 201.

[0284] Flowchart for filling discrete texture tables

[0285] Figure 12 A flowchart illustrating an exemplary process for generating an appearance model is shown. In step 601, the model generation software receives a set of available appearance attributes, which may have been determined by an appearance capture device measuring the target object 50 or by formulation software determining the components of a candidate formulation 60 (including the proportions of the components in the candidate formulation 60). In step 602, the software performs a fitting of a luminance BRDF model to the available appearance attributes. In step 603, the software populates a discrete color table based on the available appearance attributes. In step 604, the software populates a discrete texture table based on the available appearance attributes. For this purpose, it determines the target texture that forms the entries in the discrete texture table.

[0286] Flowchart for statistical texture synthesis

[0287] Figure 13A flowchart illustrating an exemplary process for determining a target texture associated with a target coordinate set is shown. In step 701, the model generation software receives multiple source textures and their associated coordinates. In step 702, the software creates a Delaunay triangulation. In step 703, the software identifies a simplex containing the target coordinates within the Delaunay triangulation. In step 704, the software determines the centroid coordinates of the target coordinates relative to the identified simplex and stores these as interpolation weights. In step 705, the software randomly selects one of the source textures at a corner of the selected simplex with probability based on the interpolation weights of the source textures. In step 706, the software randomly extracts a texture patch from the selected source textures. In step 707, the software modifies the patch to match its pixel value histogram with the average pixel value histogram. In step 708, the software inserts the modified patch into the target texture so that it seamlessly fits the existing texture content in the target texture. Steps 705 to 708 are repeated until the target texture is completely filled. The completed target texture can then be used as an entry in a discrete texture table.

[0288] Calculate the texture table of the mixture

[0289] Statistical texture synthesis can also be employed when calculating entries for a discrete texture table of materials (which are mixtures of several components, e.g., several components of a candidate material, which are mixtures of components based on a formulation determined by formulation software). In this case, source pieces can be randomly sampled from source textures associated with different components with probabilities based on their concentrations in the mixture and their interpolation weights.

[0290] Combination of different appearance capture devices

[0291] In the workflow discussed above, the appearance attributes of three types of objects are determined through measurement: target objects, test objects, and reference objects already generated for various reference materials. These measurements are typically performed in different locations. For example, in the case of vehicle repair, measurements of the target object are performed in a body shop, measurements of the test object are typically performed by the paint supplier, and measurements of the reduced reference material are typically performed in a paint development laboratory.

[0292] Therefore, these measurements will typically not be performed by the same appearance capture device. If the same type of appearance capture device is used for measurements of all three types of objects, only inter-instrument calibration is required. However, different types of appearance capture devices, potentially with varying degrees of complexity, can often be used at different locations. For example, a car body repair shop might use a relatively simple handheld multi-angle spectrophotometer without imaging capabilities (such as X-Rite's MA-5 instrument) to measure the target object, while a paint supplier might use a more complex handheld multi-angle spectrophotometer with imaging capabilities (such as X-Rite's MA-T6 or MA-T12 instrument) to measure the test object, and a paint development laboratory might use a highly complex stationary appearance capture device (such as X-Rite's TAC7 instrument) to determine the properties of a reference material, featuring numerous illumination and observation directions and highly sophisticated imaging capabilities.

[0293] The measurement geometry of a multi-angle spectrophotometer is typically defined in terms of the observation angle, illumination angle, and specular reflection angle. For example, for a pickup optics with an observation angle of 45° perpendicular to the normal of the target surface to be measured, the specular reflection angle of such an observation angle would be 45° on the opposite side of the surface normal. An illumination angle of 0° perpendicular to the surface normal forms a non-specular 45° with the observation angle and a non-specular 45° with the specular reflection, and is designated 45as45. For the same observation angle (45°), a non-specular illumination angle 25° closer to the observation angle from specular reflection will be designated 45as25. For the same observation angle, an illumination angle 15° further away from the observation angle relative to specular reflection will be designated 45as-15. For each example provided herein, the observation angle and illumination angle are interchangeable due to the principle of reciprocity. In other words, an illumination angle of 0° to the surface normal and an observation angle of 45° to the surface normal should produce the same results as an observation angle of 0° to the surface normal and an illumination angle of 45° to the surface normal.

[0294] Spectrophotometers with measurement geometries including 45as15, 45as25, 45as45, 45as75, and 45as110 will be considered pentagonal spectrophotometers. For example, adding another illumination source at -15° from the specular reflection will add a 45as-15 measurement geometry, resulting in a hexagonal spectrophotometer. In some examples, a second set of pickup optics is added at 15° to the surface normal. The specular reflection angle for such an observation angle is 15° on the opposite side of the surface normal. Maintaining the same illumination angle, this produces measurement geometries of 15as-45, 15as-15, 15as15, 15as45, and 15as80. Additional measurement geometries with an observation angle of 15° to the normal and an illumination angle of -5° to the specular surface will also be available, but may be too close to the specular surface to provide useful measurement results. There are also three angle spectrophotometers with measurement geometries such as 45as15, 45as45 and 45as70 or 45as15, 45as45 and 45as110.

[0295] Therefore, the amount of available information associated with the three types of objects may differ. This is in Figure 14 The diagram in the middle shows, Figure 14 The diagram schematically illustrates the variation of information content along three dimensions: the first dimension is the available information content of the target object, the second dimension is the available information content of the test object, and the third dimension is the available information content of the reference material. Available information content can vary along each of these dimensions between "L" ("low," color attributes are available only for a limited number of combinations of illumination and viewing directions, and no texture attributes are available) and "H" ("high," both color and texture attributes are available, each for a large number of combinations of illumination and viewing directions).

[0296] We will now discuss possible strategies for handling different levels of information content.

[0297] (a) The information content is high for the target object, test object, and reference material. Figure 14 (in the block "H / H / H")

[0298] Ideally, the information content is high for each of the three dimensions; that is, a large number of combinations of color attributes for the target material, the test material, and each reference material, as well as a considerable number of combinations of texture attributes in image form for the irradiation and observation directions, are available. In this case, sufficient information is directly available to visualize the appearance of the target material based on the measured appearance attributes of the target object and to generate a first instance of an appearance model for this purpose. Sufficient information is also available to determine suitable candidate formulations (for which a good match with the appearance of the target material is expected), to visualize the appearance of candidate formulations based on the available appearance attributes of the reference materials, and to generate a second instance of an appearance model for this purpose. Finally, sufficient information is also available to visualize the test material and to generate another instance of an appearance model for this purpose, and to evaluate whether the appearance of the test material actually matches the appearance of the target material by determining a suitable difference norm and / or by visually comparing the visualizations of the target material and the test material. The aforementioned processes of color interpolation and extrapolation, as well as statistical texture synthesis, can be used directly without modification to generate relevant instances of appearance models that include both color and texture.

[0299] (b) The information content is low for the target object, but high for the test subjects and reference materials. high ( Figure 14 (in the block "L / H / H")

[0300] In some embodiments, an appearance capture device with both spectrophotometric and imaging capabilities (i.e., high associated information content) is used to perform measurements on test and reference objects, while a simpler device with only spectrophotometric capabilities but no imaging capabilities (i.e., low associated information content) is used to perform measurements on the target object. The question then arises: how to visualize the material appearance of the target object in a realistic manner, even without the measured texture properties of that material?

[0301] In this scenario, at least two strategies are conceivable. In a simple strategy, the entire virtual object is simply visualized without textures. For this, both the first and second instances of the appearance model can be generated using an "empty" texture table—that is, a texture table whose entries are all identical and do not exhibit any spatial variations. Equivalently, an appearance model without textures can be used to visualize the virtual object.

[0302] However, this process may not produce a fairly realistic impression of the appearance of the target object and the materials of the candidate formulation, and therefore may make it difficult to determine whether these appearances actually match.

[0303] Therefore, in the preferred strategy, the texture of the target object is predicted based on known texture properties associated with the reference material. Once the test object has been produced, the texture prediction of the test object can be additionally based on the texture properties of the test object. In other words, texture information associated with the materials of the reference material and / or the test object is used to visualize the appearance of the target material. To this end, the first instance of the appearance model can be “edited” using texture information belonging to another instance of the appearance model; that is, the texture information associated with the target material is replaced or modified by texture information associated with a different material. A method for “editing” appearance properties in this manner is described in US20150032430A1, the contents of which are incorporated herein by reference in their entirety.

[0304] A similar strategy can be used if a limited number of texture properties are available for the target material, but these properties are insufficient to meaningfully generate the texture portion of a first instance of the appearance model, or contain less texture information than is available for the reference material and / or the test object. For example, the first appearance capture device could be a multi-angle spectrophotometer configured to determine color attributes and only one or a few global texture properties, such as a global roughness parameter or a global flare parameter. In contrast, the available texture properties for the reference material and / or the test object could include image data of several combinations of illumination and observation directions. In this case, the texture properties associated with the target object can be modified to include image data derived from the available image data of the reference material and / or the test object. The image data associated with the target object should, of course, be consistent with the available measured texture properties of the target object, for example, with measured global parameters such as roughness or flare parameters. This can be ensured by appropriately modifying the pixel values ​​in the available image data of the reference and / or test materials such that the global parameters calculated from the modified image data correspond respectively to the measured global parameters of the target object.

[0305] The method used to modify pixel values ​​can be very similar to the method described above in the context of statistical texture synthesis, where the texture patch is modified such that its pixel value histogram approximately matches the average pixel value histogram. Note that both the pixel value histogram and the global roughness or flicker parameter represent examples of statistical properties associated with the image data. Similar to the example of statistical texture synthesis above, in this example, the image is also modified such that one of its statistical properties (here: the global parameter calculated from the image data) approximately matches the measured statistical property of the target object (here: the measured global parameter of the target object).

[0306] The finite texture properties measured for the target object can also differ from the global parameters. For example, the measured texture properties may include a single image of a single combination of illumination and observation directions. Regardless of the exact nature of the measured texture properties, additional images of the target material can be generated by modifying the pixel values ​​of available images associated with the target material, reference material, and / or test object in such a way that at least one statistical property of the modified image data approximates the corresponding statistical property associated with the measured texture properties of the target object.

[0307] The proposed texture prediction process is based on the assumption that the texture of the target material will be quite similar to that of the candidate formulation, provided that the components of the candidate formulation belong to the same material category as the components of the target material. For example, if the target material is known to include a certain type of flake, and if the candidate formulation includes such a type of flake, it is reasonable to assume that the texture of the target material will be at least close to that of the candidate formulation.

[0308] A similar strategy can be employed if the available color attributes of the target material are insufficient to reliably fit the color attributes required by the appearance model used. This may be the case, for example, if the multi-angle spectrophotometer used does not have a sufficient number of illumination and observation direction pairs to determine the color attributes of the target material. In this case, the color attributes of the appearance model can also be determined by “editing” in a manner similar to the editing process for the texture attributes described above, using the process described below and further disclosed in US20150032430A1.

[0309] In one example, appearance measurements from higher-information measurements are used to enhance appearance measurements from lower-information measurements. For instance, at a car body repair shop, measurements of the target surface can be obtained from triangular spectrophotometers in measurement geometries of 45as15, 45as45, and 45as110. However, measurements of the corresponding reference material can have spectral information measured by hexagonal spectrophotometers in measurement geometries of 45as-15, 45as15, 45as25, 45as45, 45as75, and 45as110.

[0310] In some embodiments, measurements of reference materials from the measurement geometries of 45as-15, 45as25, and 45as75 are added to measurements from the target surface of the triangular spectrophotometer, and a color table, such as a reference color table, is calculated from the combined measurements. Figure 6 In some embodiments, the system first confirms that the target material and the reference material have similar compositions. For example, this can be achieved by inputting vehicle information (such as VIN, paint code, brand, model, and year) into the system and comparing it with data about the reference material in database 106.

[0311] In some embodiments, measurement information from measurement geometries common to the measurements is compared. For example, measurement information from triangular spectrophotometers in measurement geometries of 45as15, 45as45, and 45as110 is compared with corresponding measurement information from hexagonal spectrophotometers. Differences between the groups of measurements (such as Delta E) are determined, and correction factors or correction functions are determined and applied to the gap measurement geometries of 45as-15, 45as25, and 45as75 before adding correction factors or correction functions to the measurements of the target surface and performing color table calculations.

[0312] This can be extended to even more advanced measurement geometries and combinations. For example, the color and texture properties of a target surface can be obtained using a hexagonal spectrophotometer under measurement geometries of 45as-15, 45as15, 45as25, 45as45, 45as75, and 45as110. However, reference materials can be characterized using measurement geometries including 15as-45, 15as-15, 15as15, 15as45, and 15as80. Measurements of the target surface can be augmented at these additional measurement sites using measurements of the reference material, similar to what has been described above. In one example, such as... Figure 15 As shown, measurements at the illumination and observation angles indicated by the circled X are added to or substituted for measurements at the additional illumination and observation angles. (See reference above.) Figure 6 The color table is calculated as described. In such an example, target locations a, b, and other locations can be interpolated from measurements taken from the target surface, from a reference surface, or a combination of both.

[0313] While the foregoing examples are provided in the context of a target surface having a low level of information relative to a reference material having a higher level of information, the invention is not limited thereto. The invention can be applied to any of the examples below, where the target object, reference material, and test object have different levels of information.

[0314] (c) The information content is high for the target object and reference materials, but low for the test object. of( Figure 14 (in the block "H / L / H")

[0315] In some embodiments, image-based texture information may be available for both the target and reference materials, but no texture information or only limited texture information (e.g., one or more global texture parameters or image information for only one pair or for the illumination and viewing directions) may be available for the test subject. In this case, a strategy similar to that in case (b) may be employed. Specifically, the texture of the test material may be predicted based on known texture properties associated with the target material and / or the reference material. Furthermore, the texture may be predicted based on known texture data from the constituent materials of the paint, such as texture information obtained from the reduction of individual effect pigments.

[0316] (d) The information content is low for the target object and low for the test object, but low for... The reference material is highly informative. Figure 14 (in the block "L / L / H")

[0317] In some embodiments, image-based texture information is available only for the reference material, while no texture information or only limited texture information is available for the target and test objects. In this case, a strategy similar to that in case (b) can be employed again. Specifically, the texture of the target and test materials can be predicted based on known texture properties associated with the reference material. Furthermore, the various color measurement groups can be combined and a color table calculated, as in case (b).

[0318] (e) The information content is high for the target and test subjects, but low for the reference materials. ("H / H / L", in) Figure 14 (Not visible in the middle)

[0319] In some embodiments, image-based texture information may be available for both the target and test objects, but no texture information or only limited texture information may be available for the reference material. This may be the case, for example, if a relatively old colorant database is used, which was filled when only less sophisticated instruments were available, while more modern appearance capture devices are available to measure both the target and test materials.

[0320] In such a case, the color and / or texture information of the target material and / or test material (once the test material has been produced) can be used to visualize the appearance of the candidate formulation. For this purpose, a second instance of the associated appearance model can be “edited” using the texture information belonging to the target material and / or test material. This can be done in very the same way as the first instance of editing the appearance model in case (b) above.

[0321] Once these texture properties are available, it is advantageous to use the texture properties of the test material to visualize the candidate formulations because it can be safely assumed that the relatively small differences between the composition of the other candidate formulations and the test material will have only a negligible impact on the texture.

[0322] (f) The information content is low for the target object and reference materials, but high for the test object. ("L / H / L", in) Figure 14 (Not visible in the middle)

[0323] If image-based texture information is available for the test material, but no texture information or only limited texture information is available for the target and reference materials, the virtual object can be visualized without texture or using some generic texture, as long as the test object is not yet available. Once the test object is available, its texture properties can then be used to visualize the target material and candidate formulations. For this purpose, the texture color attribute in the associated instance of the appearance model can be edited as described above for case (b).

[0324] (g) The information content is high for the target object, but low for the test object and reference material. of( Figure 14 (in the block "H / L / L")

[0325] If image-based texture information is available for the target material, but no texture information or only limited texture information is available for the test and reference materials, a strategy similar to that for case (e) above can be employed. The texture and color attributes of the target object can be used to visualize the appearance of the candidate formulation and the test object. To this end, the texture information belonging to the target material can be used to “edit” associated instances of the appearance model.

[0326] (h) The information content is low for the target object, test object, and reference material. Figure 14 (in the block "L / L / L")

[0327] If no image-based texture information is available for any object in the object, the virtual object can be simply visualized without texture or using a generic texture of the material category to which the target object belongs.

[0328] Editing reflection values

[0329] In addition to combining measurements from instruments with different measurement geometries as described above, a set of high-information appearance data can be edited to include a set of low-information appearance data measurements, as described in US2015 / 0032430. (Reference) Figure 16 In the general case of such editing, G represents the high-information (densely distributed) BTF as measured for a first material, which may include, but is not limited to, reference material M. R For each pixel of the measurement area of ​​the first material and for a large number of illumination and observation directions, G stores a set of reflectance values, which can be, for example, RGB color values ​​or spectral values. In the following text, each set of reflectance values ​​may be referred to as a reflectance value or a color value.

[0330] In some embodiments, to obtain high-information group appearance data G, a complex reflection scanner 711 is used to measure the first material M. RA sufficiently large surface area is used to provide a set of reflectance values ​​for each pixel of the scanned surface area under a large number of illumination directions (at least 5, more preferably at least 10) and a large number of observation directions (at least 5, more preferably at least 10). In other embodiments, the surface 711a of the first material is measured using a handheld multi-angle spectrophotometer, and high-information, densely filled BTF database 711b is interpolated or extrapolated from the measurement results, as described more fully above. The result indicates M R This is the first dataset containing the measured values ​​of appearance attributes. This first dataset is also referred to as M in this paper. R The original BTF database G 712 is densely filled.

[0331] In some embodiments, the first dataset G includes a two-way texture function (BTF), a two-way scattering surface reflection distribution function (BSSRDF), a spatially varied two-way transmission distribution function (SVBTDF), or a spatially varied two-way reflection distribution function (SVBRDF).

[0332] H represents the second material M S A set of low-information (“sparsely captured”) reflectance values ​​at a measurement point (i.e., a material different from the first material). The low-information reflectance values ​​H include one or more sets of values, such as RGB values ​​or other color or spectral values. If the second material is measured under specific illumination conditions and in a specific viewing direction, only a single set of reflectance values ​​is obtained. If the second material M is measured under several illumination directions and / or viewing directions... S The number of combinations of illumination and observation directions yields multiple sets (s sets) of Hs. Suitable color and appearance measurement devices for capturing sparse reflectance values ​​include portable multi-angle spectrophotometers as disclosed herein. The measuring device used to capture these sparse reflectance values ​​is also referred to hereinafter as a simple scanner. For completeness, it should be mentioned that in practical use, reflectance values ​​H can also be taken from a database or a suitable color chart or similar source.

[0333] In one example, a simple reflective scanner or color measuring device 713 is used to measure the second material M. S The measurement points were chosen to provide the sparse reflectance value H 714. The result indicates M. S A second dataset containing at least one measured value of an appearance attribute. M S Appearance attributes include M R The second dataset contains at least one, but not all, of the appearance attributes measured above. This second dataset is also referred to in this paper as the "sparsely padded" dataset. "Sparse" means less than that measured for M. R At least 50% of the amount in the densely populated BTF database G. In some embodiments, from M SThe number of appearance attributes in the collected data is less than M R The number of appearance attributes represented in the BTF is 1%.

[0334] The data acquisition process itself is known and can be performed using any suitable measuring equipment, including, for example, a spectrophotometer. The material can be irradiated with electromagnetic (EM) radiation in the near-IR, UV, or human-detectable spectrum range. BTF data G can be compared with M... S BTF data H can be acquired simultaneously or at different times. M R BTF data B can be stored in a container used for storing data from M. S The data is on the same computer or different computers.

[0335] Continue to refer to Figure 16 G′ represents a highly informative (densely filled) BTF database derived from the original BTF G by modifying it with sparse reflectance values ​​H. Modifying a BTF is also known as editing a BTF. The modified BTF G′ represents a simulated material that combines the appearance properties of both a first (e.g., reference) material and a second material.

[0336] Since the reflectance value H will be converted to G, the reflectance value H must be of the same type as those of G, i.e., an RGB color value, a spectral value, or any other suitable color value. Otherwise, the reflectance value G or preferably the reflectance value H must be converted accordingly.

[0337] In one example, it is at least partially based on M S The data in the associated second dataset H, and at least in part based on data from M R Data from the associated first dataset G is used to determine a third dataset G′ that indicates the appearance of the simulated material. In some embodiments, data from M... R BTF G and M S The sparse reflectance values ​​H are synthesized into a new set of appearance properties. By M S The sparse reflectance value H is converted to BTF G 715 for editing (modification) M. R The original BTF G. In this synthesis step, a modified BTF G′ is produced, which is filled as densely as G, but with a structure that makes the visualization of G′ similar to that of the second material M. S The reflectance value, i.e., the reflectance value of G′, is to be as similar as possible to the reflectance value already obtained in the case of the BTF of the second material having been actually measured and characterized by the process of generating a high-information appearance dataset. This transformation may include data transformation, texture synthesis, and other transformation steps.

[0338] In some embodiments, the third dataset is determined at least in part based on data from the first and second datasets and data from the fourth dataset, which indicates standardized values ​​of appearance properties of a family of generally similar physically tangible materials that represent the first or second material as a member.

[0339] In some embodiments, the third dataset G′ is corrected so that the visualization of the G′ appearance dataset more closely matches the second material M. S The appearance of the material. In some embodiments, error values ​​are used to correct the data. In one example, the error value consists of: (a) the difference between a parameter of a measured appearance attribute of the second material and the same parameter of the corresponding appearance attribute in the third dataset, and (b) a physical plausibility value, which is based on the difference between at least one, preferably at least two, parameters of at least one, corresponding measured appearance attribute of a reference material and the same parameter of one or more corresponding measured appearance attributes in the third dataset. In some embodiments, if the error value is greater than a predetermined threshold, the third dataset is revised until the error value is less than the predetermined threshold.

[0340] The third dataset can optionally be processed to form a representation of the second material M. S An image representing the synthesized appearance. In some embodiments, the image may be 3D. Figure 1 A third dataset (e.g., an edited BTF G′) 716 is shown to be fed as input data to the rendering engine 717 and visualized on the display 718. This can include any of the visualization techniques discussed above.

[0341] One application of appearance dataset editing technology is in the field of automotive refinishing. (See the reference above.) Figure 14 The different levels of appearance information can be used for visualization at different stages of the repair process. While the example below discusses an example where the reference material has a high-information dataset and the target surface has low information, this example can be combined with any of the examples of high / low-information appearance datasets provided above. Furthermore, this aspect of the invention is not limited to the use of appearance attribute datasets that are consistently higher or lower in information than each other. For example, one appearance dataset might have high levels of color attributes, such as those from a multi-angle spectrophotometer, but low or no texture information. Another dataset might include reduced texture attribute information from effect pigments, but no color attribute information. Such datasets can be combined using the same techniques described above.

[0342] In this particular application, it's important to note that automotive paint materials are typically homogeneous, down to the deformation caused by the inclusion of certain pigments or flakes in the paint. Because automotive paint is a nearly homogeneous material, the entire measurement surface area of ​​the reference material can be included in the editing process by setting P to all pixels in that group.

[0343] A reference surface of a car paint reference is measured, and a high-information appearance dataset G is generated. As explained more fully above, the BRDF of the surface is represented using an angle-dependent color table and a Cook-Torrance BRDF model. Residuals contain local effects caused by flaking, such as flash, and are represented by specially coded BTFs. Target surfaces, such as those of a car to be repaired, are measured, and a low-information appearance dataset H is generated.

[0344] The high-information BTF G is then edited in three steps: (1) new parameters for the BRDF model are computed by matching the grayscale reflectance of the appearance dataset B with the given sparse reflectance values ​​of the appearance dataset H; (2) the entries in the color table are recomputed to match the colors to the sparsely measured reflectance values ​​H; and (3) the colors in all pixels of the slab BTF are changed to match the edit performed on the color table, and the angular distribution of the image is changed according to this edit. If no spatial information is given in H, the distribution of the slab effect in an image of the slab BTF cannot change significantly.

[0345] More specifically, in the first step, optimization is used to determine new values ​​for the diffuse and specular reflection coefficients and the roughness parameter of the Cook-Torrance model. For the second step, the original color table is first warped in the angular domain based on the average surface roughness variation. Since the dual-angular color variation of metallic paint depends on the alignment of flake particles, and since the roughness parameter reflects the degree of misalignment, this operation corrects for differences in flake alignment between the source and target paints. Furthermore, in the second step, a color operator is applied to the entries of the original color table to match colors from H. This color operator depends on the color or spectral space in which the color table is defined. Typical transformations have only a few parameters. An example would be hue and saturation variation operators. In the third step, a modified flake BTF is created by applying the same angular warp and color transformation from the second step to all pixels of the flake BTF. The result is a modified appearance dataset G′. The modified appearance dataset G′ is visualized together with a reference appearance dataset G using any of the appearance and / or object models disclosed in this paper.

[0346] Reference List

[0347] [Ber19] Roy S. Berns, “Billmeyer and Saltzman’s Principles of Color Technology”, 4th ed., Wiley, 2019, p. 184.

[0348] [Kw03]Kwatra, V., A., Essa, I., Turk, G., and Bobick, A., “Graphcuttextures: image and video synthesis using graph cuts”, ACM Transactions on Graphics (ToG), 2003, 22(3), pp. 277-286.

[0349] [MTG+19] Gero Müller, Jochen Tautges, Alexander Gress, Martin Rump, Max Hermann and Francis Lamy, “AxF-Appearance exchange Format”, version 1.7, April 11, 2019, is available upon request from X-Rite.

[0350] [RMS+08] Martin Rump, Gero Müller, Ralf Sarlette, Dirk Koch and Richard Klein, "Photo-realistic Rendering of Metallic Car Paint from Image-Based Measurements", in: R. Scopigno, E. (Edited), Computer Graphics Forum, Eurographics, April 2008, 27:2 (527-536).

[0351] [RSK09] Martin Rump, Ralf Sarlette, and Richard Klein, “Efficient Resampling, Compression and Rendering of Metallic and Pearlescent Paint”, in: M. Magnus, B. Rosenhahn, H. Theisel (eds.), Proceedings of Vision, Modeling, and Visualization, November 2009, pp. 11-18.

[0352] [Rus98] Szymon M. Rusinkiewicz, “A New Change of Variables for EfficientBRDF Representation,” in: G. Drettakis G., N. Max (eds.), Rendering Techniques'98, Eurographics, Springer, Vienna, 1998. https: / / doi.org / 10.1007 / 978-3-7091-6453-2_ 2。

[0353] [Sh68] Shepard D., "A two-dimensional interpolation function forirregularly-spaced data", in: Proceedings of the 1968 23rd ACM nationalconference, 1968, pp. 517-524.

[0354] [W98] Williams, CK, "Prediction with Gaussian processes: From linearregression to linear prediction and beyond", in: Learning in graphical model, Springer, Dordrecht, 1998, pp. 599-621.

Claims

1. A computer-implemented method for visualizing the appearance of at least two materials, the method comprising: Obtain the first set of appearance attributes, the appearance attributes in the first set are associated with the first material, the first set of appearance attributes includes the measured appearance attributes (54). Obtain the second set of appearance attributes, which are associated with the second material; Obtain a geometric model of at least one virtual object (72), the geometric model defining the three-dimensional macroscopic surface geometry of the virtual object (72); The third set of appearance attributes is synthesized from the first set of appearance attributes and the second set of appearance attributes; and The scene including the at least one virtual object (72) is visualized using a display device (70), a third set of appearance attributes, a comparison set of appearance attributes, and the geometric model, wherein the comparison set of appearance attributes includes a second set of appearance attributes, the third set of appearance attributes is used to visualize a first portion (72a) of the at least one virtual object (72), and the comparison set of appearance attributes is used to visualize a second portion (72b) of the at least one virtual object (72), so as to allow a direct visual comparison between the first set of appearance attributes modified by the second set of appearance attributes and the comparison set of appearance attributes.

2. The computer-implemented method according to claim 1, wherein, The first set of appearance attributes includes color attributes but not texture attributes; The second set of appearance attributes includes both color and texture attributes, and The third set of appearance attributes includes a combination of color attributes from the first set of appearance attributes and texture attributes from the second set of appearance attributes.

3. The computer-implemented method according to claim 1 or 2, wherein, The first set of appearance attributes includes a first plurality of color attributes corresponding to a first plurality of optical measurement geometries; The second set of appearance attributes includes a second set of color attributes corresponding to a second set of optical measurement geometries, wherein the second set of measurement geometries is larger than the first set of measurement geometries; and The third set of appearance attributes includes a combination of color attributes derived from the first and second sets of color attributes.

4. The computer-implemented method according to claim 3, wherein, In the third set of appearance attributes, at the measurement geometry that matches the first set of color attributes, the color attribute in the second set of color attributes is replaced by the first set of multiple color attributes.

5. The computer-implemented method according to claim 4, wherein, In the third group of appearance attributes, the color attributes in the second group of color attributes that were not replaced by the first group of color attributes are corrected in order to adjust the color difference between the replaced color attribute and the replaced color attribute.

6. The computer-implemented method according to claim 1 or 2, wherein, The method includes: Generate the first instance of the appearance model (56), the first instance of the appearance model includes the third set of appearance attributes; and A second instance (66) of the appearance model is generated, which includes the appearance attributes of the comparison group. The appearance model includes a discrete texture table, which comprises multiple target textures (201). Each target texture (201) is represented by image data and associated with a different target coordinate set (a'), which indicates a specific combination of illumination and viewing directions. The first and second instances of the appearance model are used to visualize the at least one virtual object (72).

7. The computer-implemented method according to claim 6, in, Generating the first instance of the appearance model (56) includes at least one of the following operations: Interpolation is performed between available appearance attributes under different combinations of illumination and observation directions; as well as Extrapolate the available appearance attributes from the selected combination of illumination and observation directions; Among them, the available appearance attributes include appearance attributes from the third group of appearance attributes.

8. The computer-implemented method according to claim 1 or 2, wherein, The first material includes the target vehicle paint, and the first set of appearance attributes includes the measured appearance attributes of the target vehicle paint (54). The second material includes reference automotive paint, and the second set of appearance properties includes the measured appearance properties of the reference automotive paint (54).

9. The computer-implemented method according to claim 1 or 2, wherein, The first material includes the target vehicle paint, and the first set of appearance attributes includes the measured appearance attributes of the target vehicle paint (54). The second material includes candidate formulations of automotive paint, and the second set of appearance properties includes calculated appearance properties of the candidate formulations (54).

10. The computer-implemented method according to claim 1 or 2, wherein, The first material includes the paint of the test object, the automobile, and the first set of appearance attributes includes the measured appearance attributes of the test object (54). and The second material includes reference automotive paint, and the second set of appearance properties includes the measured appearance properties of the reference automotive paint (54).

11. The computer-implemented method according to claim 1 or 2, wherein, The first material includes candidate automotive paint formulations, and the first set of appearance attributes includes measured appearance attributes of candidate automotive paint formulations (54). The second material includes the target automotive paint, and the second set of appearance attributes includes the measured appearance attributes of the target automotive paint (54).

12. The computer-implemented method according to claim 1 or 2, wherein, The third group of appearance attributes also includes appearance attributes from the fourth group of appearance attributes.

13. The computer-implemented method according to claim 12, wherein, The fourth group of appearance attributes includes reduced appearance attributes from one or more pigments associated with paint formulation.

14. The computer-implemented method according to claim 1, wherein, The second set of appearance attributes includes reduced appearance attributes from one or more pigments associated with paint formulation.

15. The computer-implemented method according to claim 1, wherein, The second set of appearance attributes includes calculated appearance attributes corresponding to one or more pigments associated with paint formulation.

Images