A woven fabric capture method and system based on reflection-transmission photo pairs

Abstract

The present application relates to the field of computer graphics, and provides a woven fabric capturing method and system based on reflection-transmission photo pairs. The method comprises: obtaining a fabric reflection image and a fabric transmission image based on a woven fabric to be captured; obtaining fabric parameters by using a prediction model based on the fabric reflection image and the fabric transmission image; describing the woven fabric to be captured as a first double-layer woven fabric model based on SpongeCake; wherein the warp yarn and the weft yarn of the first double-layer woven fabric model each have different material parameters, and the same kind of yarn of the warp yarn and the weft yarn has the same material parameter, so that the fabric transmission image shows the double-layer feature; describing aggregated micro-thin sheets by using an azimuth-invariant phase function, substituting the phase function into the first double-layer woven fabric model to obtain a second double-layer woven fabric model; and after substituting the fabric parameters into the second double-layer woven fabric model, rendering the woven fabric to be captured.

Description

Technical Field

[0001] This invention relates to the field of computer graphics, and more particularly to a method and system for capturing woven fabric based on reflection-transmission photo pairs. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] Current research on woven fabrics estimates fabric parameters from photographs with different rendering effects. However, it only considers the reflected image, and parameters derived solely from reflection cannot provide enough information to reproduce all fabric parameters. The most obvious example is thickness, which is insensitive to reflection but strongly affects transmission. Most other parameters also influence transmission; therefore, current methods reduce the accuracy of fabric parameter estimation. Summary of the Invention

[0004] To address the technical problems mentioned above, this invention provides a method and system for capturing woven fabric based on reflection-transmission photo pairs. This invention designs a more realistic fabric material model that includes transmission and recovers the corresponding fabric material parameters by taking photos of real fabric.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] The first aspect of the present invention provides a method for capturing woven fabric based on reflective-transmittance photo pairs.

[0007] A method for capturing images in woven fabric based on reflective-transmittive photo pairs, comprising:

[0008] Based on the woven fabric to be captured, acquire the fabric reflection image and the fabric transmission image;

[0009] Based on the fabric reflection and transmission images, a prediction model is used to obtain the fabric parameters.

[0010] The fabric to be captured is described as a first double-layer woven fabric model based on SpongeCake; wherein the warp and weft yarns of the first double-layer woven fabric model have different material parameters, and the same type of yarn in the warp and weft yarns has the same material parameters, so that the fabric transmission image shows double-layer features.

[0011] The aggregated microsheet is described by a phase function with constant azimuth angle. The phase function is then substituted into the first double-layer woven fabric model to obtain the second double-layer woven fabric model.

[0012] After substituting the fabric parameters into the second double-layer woven fabric model, the woven fabric to be captured is rendered.

[0013] Furthermore, the process of adopting the prediction model includes: inputting the fabric reflection image and the fabric transmission image into the prediction model to obtain the fabric reflection feature vector and the fabric transmission feature vector; concatenating the fabric reflection feature vector and the fabric transmission feature vector, and then passing them sequentially through a multilayer perceptron and a fully connected layer to obtain the output vector; and mapping the output vector to the parameter space to obtain the fabric parameters.

[0014] Furthermore, the prediction model training process includes: when rendering using the second double-layer woven fabric model, optimizing the fabric parameters using a loss function.

[0015] Furthermore, the loss function comprises a color loss function, a Gram loss function, and a prior loss function. The color loss function is obtained by downsampling the target image and the rendered image respectively, and then calculating the L1 loss function between the images. The Gram loss function is obtained by inputting the target image and the rendered image into the prediction model respectively, and calculating the L1 loss function between the Gram matrices. The prior loss function is obtained by calculating the Gaussian loss function from the mean and variance of the cloth parameters.

[0016] Furthermore, in the constructed first double-layer woven fabric model or second double-layer woven fabric model, a tension-based thickness scaling method is adopted, with the specific formula as follows:

[0017] T = T × (S) min +μ×(1-S min ),

[0018] Where T represents thickness, S min This represents the minimum thickness scaling ratio, where μ is the tension factor.

[0019] Furthermore, the phase function is:

[0020]

[0021] Where, ω h 'Indicates ω o Rotate to ω i After that, on the same vertical plane, the half-length vector between the two.

[0022] Furthermore, the bidirectional scattering distribution function of the first double-layer woven fabric model or the second double-layer woven fabric model is:

[0023]

[0024] Among them, f s This indicates the use of SGGX to describe the single scattering formula for a bilayer SpongeCake microsheet; f mA single scattering formula for bilayer SpongeCake of polymerized microsheets is used to describe the ASGGX formula, which is used to approximate multiple scattering. This is the diffuse reflection term.

[0025] A second aspect of the invention provides a woven fabric capture system based on a reflective-transmittance photo pair.

[0026] A woven fabric capture system based on reflective-transmittive photo pairs includes:

[0027] The image acquisition module is configured to acquire fabric reflection and fabric transmission images based on the woven fabric to be captured.

[0028] The prediction module is configured to obtain fabric parameters based on fabric reflection and fabric transmission images using a prediction model.

[0029] The description module is configured to describe the woven fabric to be captured as a first double-layer woven fabric model based on SpongeCake; wherein the warp and weft yarns of the first double-layer woven fabric model have different material parameters, and the same type of yarn in the warp and weft yarns has the same material parameters, so that the fabric transmission image shows double-layer features.

[0030] The substitution module is configured to: use a phase function with an invariant azimuth angle to describe the aggregated microsheet, and substitute the phase function into the first double-layer woven fabric model to obtain the second double-layer woven fabric model;

[0031] The rendering module is configured to: substitute the fabric parameters into the second double-layer woven fabric model, and then render the woven fabric to be captured.

[0032] A third aspect of the present invention provides a computer-readable storage medium.

[0033] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the woven fabric capture method based on reflective-transmittance photo pairs as described in the first aspect above.

[0034] A fourth aspect of the present invention provides a computer device.

[0035] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements the steps of the woven fabric capture method based on reflective-transmittance photo pairs as described in the first aspect above.

[0036] Compared with the prior art, the beneficial effects of the present invention are:

[0037] This invention proposes a SpongeCake-based double-layer woven fabric model, which has more realistic details compared to the previous single-layer model, can better present the double-layer characteristics of woven fabric, match the simulation results of multiple scattering, and avoid the gaps in transmission of previous methods.

[0038] This invention proposes an azimuth-independent phase function (ASGGX) to describe polymer microsheets, which can be used to better match the multiple scattering behavior of real fabrics.

[0039] The present invention obtains two images of reflection and transmission, and by using neural networks and differentiable rendering, it can recover various fabric parameters, including thickness, and has reasonable transmission performance. Attached Figure Description

[0040] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0041] Figure 1 This is a flowchart illustrating the woven fabric capture method based on reflective-transmittive photo pairs as shown in this invention;

[0042] Figure 2 This is a schematic diagram of the two-layer model in step one of the method of the present invention;

[0043] Figure 3 This is a real-life illustration of the double-layer structure of satin fabric;

[0044] Figure 4 This is a schematic diagram of tension-based thickness scaling in step one of the methods of the present invention;

[0045] Figure 5 This is a coordinate system diagram of the formula in step two of the method of the present invention;

[0046] Figure 6 This is a schematic diagram of the capture configuration in step three of the method of the present invention;

[0047] Figure 7 This is a schematic diagram of the structure of the parameter prediction network in step four of the method of the present invention;

[0048] Figure 8 The positive rendering result of this invention illustrates the contribution of the multiple scattering component, with a comparison diagram using simulation results as a reference.

[0049] Figure 9 The positive rendering result of this invention illustrates the contribution of the two-layer model, with a real-world photograph as a reference.

[0050] Figure 10This is a comparison diagram of the recovery results of the present invention with those of the prior art. Detailed Implementation

[0051] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0052] It should be noted that the following detailed description is illustrative and intended to provide further explanation of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0053] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0054] It should be noted that the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of methods and systems according to various embodiments of this disclosure. It should be noted that each block in a flowchart or block diagram may represent a module, segment, or portion of code, which may include one or more executable instructions for implementing the logical functions specified in the various embodiments. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than that shown in the drawings. For example, two consecutively represented blocks may actually be executed substantially in parallel, or they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the flowcharts and / or block diagrams, and combinations of blocks in the flowcharts and / or block diagrams, may be implemented using a dedicated hardware-based system that performs the specified functions or operations, or using a combination of dedicated hardware and computer instructions.

[0055] Terminology Explanation:

[0056] SpongeCake is a multi-layered surface material based on a microsheet model. It uses fibrous microsheets to represent fabric materials.

[0057] Example 1

[0058] This embodiment provides a method for capturing images on woven fabric based on reflection-transmission photo pairs. This embodiment uses the application of this method to a server as an example for illustration. It is understood that this method can also be applied to a terminal, or to a system including a terminal, server, and system, and is implemented through interaction between the terminal and server. The server can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network servers, cloud communication, middleware services, domain name services, CDN security services, and big data and artificial intelligence platforms. The terminal can be a smartphone, tablet, laptop, desktop computer, smart speaker, smartwatch, etc., but is not limited to these. The terminal and server can be directly or indirectly connected via wired or wireless communication, which is not limited herein. In this embodiment, the method includes the following steps:

[0059] Based on the woven fabric to be captured, acquire the fabric reflection image and the fabric transmission image;

[0060] Based on the fabric reflection and transmission images, a prediction model is used to obtain the fabric parameters.

[0061] The fabric to be captured is described as a first double-layer woven fabric model based on SpongeCake; wherein the warp and weft yarns of the first double-layer woven fabric model have different material parameters, and the same type of yarn in the warp and weft yarns has the same material parameters, so that the fabric transmission image shows double-layer features.

[0062] The aggregated microsheet is described by a phase function with constant azimuth angle. The phase function is then substituted into the first double-layer woven fabric model to obtain the second double-layer woven fabric model.

[0063] After substituting the fabric parameters into the second double-layer woven fabric model, the woven fabric to be captured is rendered.

[0064] The following is a detailed description of this embodiment with reference to the accompanying drawings, such as... Figure 1 As shown:

[0065] Step 1: According to Figure 6 The image capture configuration shown is illustrated in the figure, with specific location settings as indicated. Shooting should be in RAW format with fixed camera parameters such as white balance, exposure, and exposure time. Before shooting, calibrate the light source intensity using a standard colorimeter. During shooting, first turn on the left light source to capture the fabric reflection image, then turn off the left light source and turn on the right light source to capture the fabric transmission image. The initial resolution of the obtained raw image is 4K, which needs to be cropped and downsampled to 512x512 before subsequent network prediction and differentiable rendering optimization.

[0066] Step 2: Build a network based on pre-trained VGG, with the network structure as follows: Figure 7 As shown, the reflection and transmission images are input into VGG respectively to obtain their respective Gram matrices, which are then expanded into feature vectors of length 610304. The two feature vectors are then concatenated and input into a 3-layer Multilayer Perceptron (MLP). Each layer of the MLP contains 256 nodes and a LeakyReLU activation function. The final layer outputs a 34-dimensional vector. The first five dimensions are processed by Softmax to obtain the pattern category, and the remaining dimensions represent all material parameters. These parameters are then processed by Sigmoid and remapped back to the parameter space.

[0067] The network is trained using a large amount of synthetic data, first by building a similar network in a virtual scene. Figure 6 The configuration used point light sources as approximate light sources. Based on certain priors, a large number of fabric parameters were randomly generated. The forward model described in steps four and five was used for rendering. The generated synthetic dataset included five fabric styles (plain weave, twill, satin, and twill and satin rotated 90 degrees), with 1280 pairs of reflection-transmission images generated for each style. Training used the Adam optimizer with a batch size of 32 and an initial learning rate of 0.0001. During training, the pre-trained VGG network parameters remained fixed, and only the MLP parameters were optimized. Training took 4 hours on a single A40 graphics card.

[0068] Step 3: Implement a differentiable forward rendering model in the PyTorch framework. Use the initial parameters predicted by the network to perform differentiable rendering to further optimize the parameters. The loss function includes:

[0069] (1) Color loss: The target image and the rendered image are each downsampled to 16x16, and then the L1 loss between the images is calculated.

[0070] (2) Gram loss: L1 loss between the Gram matrices obtained by inputting VGG into the target image and the rendered image respectively.

[0071] (3) Prior loss: Based on the known characteristics of the fabric, such as the mean and variance of parameters like gap size and height field scaling ratio, add Gaussian loss for these parameters.

[0072] During the optimization process, a transmitted point light source was projected onto the fabric plane, forming a Gaussian halo at the center of the image to simulate the out-of-focus effect of a real camera. This Gaussian halo only produces direct transmitted light at the gaps in the fabric, and its intensity was set to 8, with a variance of 20 pixels.

[0073] The optimization was performed using the Adam optimizer with a learning rate of 0.01 and 300 rounds of optimization, which took 4 minutes on an NVIDIA 4060 GPU.

[0074] Step 4: The forward rendering process describes the cloth as follows: Figure 2 The double-layer SpongeCake model shown has different material parameters for the warp and weft yarns, but the same type of yarn in both layers has the same material parameters. The double-layer feature should be visible in the transmission image of the satin weave, as shown below. Figure 3 As shown; a tension-based thickness scaling method is used, the specific formula of which is:

[0075] T = T × (S) min +μ×(1-S min ),

[0076] Where T represents thickness, S min This represents the minimum thickness scaling factor, set to 0.5 for satin and twill fabrics and 1.0 for plain weave fabrics; μ is the tension factor, 0.0 at the center of the long float and 1.0 at the center of the short sinker, with linear interpolation between these two values. Figure 4 As shown.

[0077] The complete model's two-way scattering distribution function (BSDF) is:

[0078]

[0079] Among them, f s This indicates the formula for single scattering of a bilayer SpongeCake microsheet using the phase function (SGGX) of the fibrous microsheet; f m A single scattering formula for bilayer SpongeCake of polymerized microsheets is used to describe the ASGGX formula, which is used to approximate multiple scattering. This is the diffuse reflection term, used to approximate more complex multiple scattering that is still difficult to model. It includes both reflection and refraction components of diffuse reflection, and the specific formula is as follows:

[0080]

[0081]

[0082] It is actually an interpolation of two Lambertian terms, where w is a weighting coefficient that controls the weight of the macro surface normal and the yarn normal; where diffuse reflection is only related to the first layer of yarn normal, while diffuse transmission is related to the normals of both layers of yarn.

[0083] Step 5: The forward rendering process uses an azimuth-invariant phase function (ASGGX) to describe the aggregated thin sheets (in the thin sheet model, multiple thin sheets are aggregated together and regarded as a large thin sheet; ASGGX can be regarded as the phase function of this large thin sheet). The specific formula for the phase function is:

[0084]

[0085] Where, ω h 'Indicates ω o Rotate to ω i After that, on the same longitudinal plane, the half-range vector between the two. See details. Figure 5 The definitions of D and σ are the same as those for SGGX. Substituting this phase function into the SpongeCake framework and using different thickness and roughness parameters than for single scattering, the formula for multiple scattering BSDF is obtained as follows:

[0086]

[0087] Among them, D m and G m It has the same normal distribution and geometric terms as SGGX and SpongeCake, except that it uses independent thickness and roughness parameters for multiple scattering lobes.

[0088] Step Six: Input the optimized material parameters obtained in Step Three into the forward rendering model described in Steps Four and Five for rendering. This model can be used in any situation requiring the rendering of woven fabric and has reflection and transmission properties close to the real fabric being photographed. For example, in film, animation, or game creation, creators may want to reference a real piece of fabric to present a similar material in their work, but do not want to manually adjust various parameters. The creator can use the capture configuration of this invention to photograph images of real fabric to reproduce it in a virtual scene.

[0089] Using this invention, more realistic woven fabric details can be rendered, and the simulation results of multiple scattering can be better matched, such as... Figure 8 As shown, this invention avoids the transmission gaps inherent in previous methods; it better presents the double-layer characteristics of woven fabrics, such as... Figure 9 As shown. Simultaneously, this invention can recover the correct fabric material parameters, including thickness, and exhibits reasonable transmission performance, such as... Figure 10 As shown.

[0090] Example 2

[0091] This embodiment provides a woven fabric capture system based on reflective-transmittance photo pairs.

[0092] A woven fabric capture system based on reflective-transmittive photo pairs includes:

[0093] The image acquisition module is configured to acquire fabric reflection and fabric transmission images based on the woven fabric to be captured.

[0094] The prediction module is configured to obtain fabric parameters based on fabric reflection and fabric transmission images using a prediction model.

[0095] The description module is configured to describe the woven fabric to be captured as a first double-layer woven fabric model based on SpongeCake; wherein the warp and weft yarns of the first double-layer woven fabric model have different material parameters, and the same type of yarn in the warp and weft yarns has the same material parameters, so that the fabric transmission image shows double-layer features.

[0096] The substitution module is configured to: use a phase function with an invariant azimuth angle to describe the aggregated microsheet, and substitute the phase function into the first double-layer woven fabric model to obtain the second double-layer woven fabric model;

[0097] The rendering module is configured to: substitute the fabric parameters into the second double-layer woven fabric model, and then render the woven fabric to be captured.

[0098] It should be noted that the image acquisition module, prediction module, description module, substitution module, and rendering module described above are the same examples and application scenarios implemented in the steps of Embodiment 1, but are not limited to the content disclosed in Embodiment 1. It should also be noted that the above modules, as part of the system, can be executed in a computer system such as a set of computer-executable instructions.

[0099] Example 3

[0100] This embodiment provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps in the woven fabric capture method based on reflection-transmission photo pairs as described in Embodiment 1 above.

[0101] Example 4

[0102] This embodiment provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps in the woven fabric capture method based on reflection-transmission photo pairs as described in Embodiment 1 above.

[0103] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of hardware embodiments, software embodiments, or embodiments combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage and optical storage) containing computer-usable program code.

[0104] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0105] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0106] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0107] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0108] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for capturing images in woven fabric based on reflective-transmittive photo pairs, characterized in that, include: Based on the woven fabric to be captured, acquire the fabric reflection image and the fabric transmission image; Based on the fabric reflection and transmission images, a prediction model is used to obtain the fabric parameters. The fabric to be captured is described as a first double-layer woven fabric model based on SpongeCake; wherein the warp and weft yarns of the first double-layer woven fabric model have different material parameters, and the same type of yarn in the warp and weft yarns has the same material parameters, so that the fabric transmission image shows double-layer features. The aggregated microsheet is described by a phase function with constant azimuth angle. The phase function is then substituted into the first double-layer woven fabric model to obtain the second double-layer woven fabric model. In the constructed first or second double-layer woven fabric model, a tension-based thickness scaling method is adopted, with the specific formula as follows: in, Indicates thickness, This indicates the minimum thickness scaling ratio. It is the tension factor; The phase function is: in, Indicates will Rotate to After they are on the same longitudinal plane, the half-length vector between them; The bidirectional scattering distribution function of the first double-layer woven fabric model or the second double-layer woven fabric model is: in, This indicates the use of SGGX to describe the single scattering formula for a bilayer SpongeCake microsheet; A single scattering formula for bilayer SpongeCake of polymerized microsheets is used to describe the ASGGX formula, which is used to approximate multiple scattering. For diffuse reflection; After substituting the fabric parameters into the second double-layer woven fabric model, the woven fabric to be captured is rendered.

2. The woven fabric capture method based on reflective-transmittive photo pairs according to claim 1, characterized in that, The process of using the prediction model includes: inputting the fabric reflection image and the fabric transmission image into the prediction model to obtain the fabric reflection feature vector and the fabric transmission feature vector; concatenating the fabric reflection feature vector and the fabric transmission feature vector, and then passing them sequentially through a multilayer perceptron and a fully connected layer to obtain the output vector; and mapping the output vector to the parameter space to obtain the fabric parameters.

3. The woven fabric capture method based on reflective-transmittive photo pairs according to claim 1, characterized in that, The training process of the prediction model includes: when rendering using the second double-layer woven fabric model, optimizing the fabric parameters using a loss function.

4. The woven fabric capture method based on reflective-transmittive photo pairs according to claim 3, characterized in that, The loss function comprises a color loss function, a Gram loss function, and a prior loss function. The color loss function is obtained by downsampling the target image and the rendered image respectively, and then calculating the L1 loss function between the images. The Gram loss function is obtained by inputting the target image and the rendered image into the prediction model respectively, and calculating the L1 loss function between the Gram matrices. The prior loss function is obtained by calculating the Gaussian loss function from the mean and variance of the cloth parameters.

5. A woven fabric capture system based on reflective-transmittive photo pairs, characterized in that, include: The image acquisition module is configured to acquire fabric reflection and fabric transmission images based on the woven fabric to be captured. The prediction module is configured to obtain fabric parameters based on fabric reflection and fabric transmission images using a prediction model. The description module is configured to describe the woven fabric to be captured as a first double-layer woven fabric model based on SpongeCake; wherein the warp and weft yarns of the first double-layer woven fabric model have different material parameters, and the same type of yarn in the warp and weft yarns has the same material parameters, so that the fabric transmission image shows double-layer features. The substitution module is configured to: use a phase function with an invariant azimuth angle to describe the aggregated microsheet, and substitute the phase function into the first double-layer woven fabric model to obtain the second double-layer woven fabric model; In the constructed first or second double-layer woven fabric model, a tension-based thickness scaling method is adopted, with the specific formula as follows: in, Indicates thickness, This indicates the minimum thickness scaling ratio. It is the tension factor; The phase function is: in, Indicates will Rotate to After they are on the same longitudinal plane, the half-length vector between them; The bidirectional scattering distribution function of the first double-layer woven fabric model or the second double-layer woven fabric model is: in, This indicates the use of SGGX to describe the single scattering formula for a bilayer SpongeCake microsheet; A single scattering formula for bilayer SpongeCake of polymerized microsheets is used to describe the ASGGX formula, which is used to approximate multiple scattering. For diffuse reflection; The rendering module is configured to: substitute the fabric parameters into the second double-layer woven fabric model, and then render the woven fabric to be captured.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps in the woven fabric capture method based on reflective-transmittance photo pairs as described in any one of claims 1-4.

7. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps in the woven fabric capture method based on reflective-transmittance photo pairs as described in any one of claims 1-4.

Images