PBR material map generation method, three-dimensional object material scanning method and device, electronic device, storage medium and computer program product

Abstract

The invention relates to a PBR material map generation method, a three-dimensional object material scanning method and device, an electronic device, a storage medium and a computer program product, and the PBR material map generation method comprises the steps: collecting a plurality of original images of different illumination conditions corresponding to a target object at a target visual angle, the plurality of original images have a target resolution; and according to the plurality of original images, performing multi-stage map prediction by using a PBR material map prediction model, and determining a target PBR material map of the target object under the target view angle, the target PBR material map having the target resolution. According to the embodiment of the invention, the material scanning efficiency of the target object at the target visual angle is effectively improved.

Description

Technical Field

[0001] The present disclosure relates to the field of computer technology, and in particular to a PBR material map generation method, a three-dimensional object material scanning method and device, an electronic device, a storage medium and a computer program product. Background Art

[0002] For virtual filming, it often takes a long time from concept design to final on-screen filming. The most time-consuming part is usually the asset production process. Unlike the production of traditional virtual scenes, the virtual scenes used for virtual filming usually need to be matched with the set scenery during filming. Therefore, how to quickly and accurately scan and restore the set scenery material to the virtual scene is particularly important, which can greatly improve the efficiency and quality of virtual filming scene production. Summary of the invention

[0003] The present invention discloses a technical solution of a PBR material map generation method, a three-dimensional object material scanning method and device, an electronic device, a storage medium and a computer program product.

[0004] According to one aspect of the present disclosure, a PBR material map generation method is provided, comprising: collecting a plurality of original images of different lighting conditions corresponding to a target object at a target viewing angle, wherein the plurality of original images have a target resolution; performing multi-stage map prediction using a PBR material map prediction model based on the plurality of original images to determine a target PBR material map of the target object at the target viewing angle, wherein the target PBR material map has the target resolution.

[0005] In a possible implementation, the method uses a PBR material map prediction model to perform multi-stage map prediction based on the multiple original images to determine the target PBR material map of the target object at the target perspective, including: for the j-th stage map prediction, based on the multiple original images, determining a plurality of images to be processed corresponding to the j-th stage map prediction, wherein j is a positive integer greater than or equal to 1; using the PBR material map prediction model, performing pixel-by-pixel map prediction on the plurality of images to be processed corresponding to the j-th stage map prediction to obtain the j-th stage PBR material map; when the resolution of the j-th stage PBR material map reaches the target resolution, determining the j-th stage PBR material map as the target PBR material map.

[0006] In one possible implementation, the resolution of multiple images to be processed corresponding to the j-th stage texture prediction is greater than the resolution of multiple images to be processed corresponding to the j-1-th stage texture prediction, and is less than or equal to the target resolution; the resolution of the j-th stage PBR material map is equal to the resolution of the multiple images to be processed corresponding to the j-th stage texture prediction.

[0007] In a possible implementation, the PBR material map prediction model includes: a first prediction sub-model and a second prediction sub-model; the multi-stage map prediction is performed based on the multiple original images using the PBR material map prediction model to determine the target PBR material map of the target object at the target perspective, including: based on the multiple original images, using the first prediction sub-model to perform a first-stage map prediction to obtain a first-stage PBR material map; based on the multiple original images and the i-1th stage PBR material map, using the second prediction sub-model to perform an i-th stage map prediction to obtain an i-th stage PBR material map, wherein i is a positive integer greater than 1; when the resolution of the i-th stage PBR material map reaches the target resolution, the i-th stage PBR material map is determined as the target PBR material map.

[0008] In a possible implementation, the first prediction sub-model is used to perform a first stage texture prediction based on the multiple original images to obtain a first stage PBR material map, including: performing downsampling processing on the multiple original images to obtain a plurality of images to be processed corresponding to the first stage texture prediction, wherein the resolution of the plurality of images to be processed corresponding to the first stage texture prediction is less than the target resolution; and the plurality of images to be processed corresponding to the first stage texture prediction are input into the first prediction sub-model to perform a first stage texture prediction to obtain the first stage PBR material map.

[0009] In a possible implementation, the method of using the second prediction sub-model to perform an i-stage texture prediction based on the multiple original images and the i-1-stage PBR texture map to obtain the i-stage PBR texture map includes: performing downsampling processing on the multiple original images to obtain a plurality of images to be processed corresponding to the i-stage texture prediction, wherein the resolution of the plurality of images to be processed corresponding to the i-stage texture prediction is greater than the resolution of the plurality of images to be processed corresponding to the i-1-stage texture prediction, and is less than or equal to the target resolution; performing upsampling processing on the i-1-stage PBR texture map to obtain a PBR material map to be processed corresponding to the i-stage texture prediction, wherein the PBR material map to be processed corresponding to the i-stage texture prediction has the same resolution as the plurality of images to be processed corresponding to the i-stage texture prediction; and inputting the plurality of images to be processed corresponding to the i-stage texture prediction and the PBR material map to be processed corresponding to the i-stage texture prediction into the second prediction sub-model to perform an i-stage texture prediction to obtain the i-stage PBR material map.

[0010] In a possible implementation, the method uses the second prediction sub-model to perform i-stage texture prediction based on the multiple original images and the i-1-stage PBR material map to obtain the i-stage PBR material map, including: fusing the multiple original images and the i-1-stage PBR material map to obtain a fused image; sampling the fused image to obtain a to-be-processed image corresponding to the i-stage texture prediction, wherein a resolution of the to-be-processed image corresponding to the i-stage texture prediction is greater than a resolution of the to-be-processed image corresponding to the i-1-stage texture prediction, and is less than or equal to the target resolution; and inputting the to-be-processed image corresponding to the i-stage texture prediction into the second prediction sub-model to perform i-stage texture prediction to obtain the i-stage PBR material map.

[0011] In a possible implementation, the method of performing multi-stage texture prediction using a PBR material texture prediction model based on the multiple original images to determine the target PBR material texture of the target object at the target perspective includes: when the target resolution is higher than a resolution threshold corresponding to the PBR material texture prediction model, performing image block splitting on each original image to obtain multiple image block groups, wherein the multiple image blocks included in each image block group correspond to the same position area in the multiple original images, and the resolution of the multiple image blocks included in each image block group is less than or equal to the resolution threshold; performing multi-stage texture prediction using the PBR material texture prediction model based on the multiple image blocks included in each image block group to determine the PBR material texture block corresponding to each image block group; and fusing the PBR material texture blocks corresponding to the multiple image block groups to obtain the target PBR material texture.

[0012] According to one aspect of the present disclosure, a three-dimensional object material scanning method is provided, comprising: acquiring multiple original images of different lighting conditions corresponding to a target object acquired at multiple acquisition viewing angles, wherein the multiple acquisition viewing angles can surround and cover the target object, and the multiple original images acquired at each acquisition viewing angle have a target resolution; performing multi-stage texture prediction using a PBR material map prediction model based on the multiple original images acquired at each acquisition viewing angle to determine a target PBR material map of the target object at each acquisition viewing angle, wherein the target PBR material map at each acquisition viewing angle has the target resolution; performing three-dimensional reconstruction of the target object based on the multiple original images acquired at each acquisition viewing angle to obtain an initial three-dimensional model corresponding to the target object; performing material mapping on the initial three-dimensional model based on the target PBR material map of the target object at each acquisition viewing angle to obtain a target three-dimensional model corresponding to the target object.

[0013] According to one aspect of the present disclosure, a three-dimensional object material scanning device is provided, comprising: a carrying part, an image acquisition part and a plurality of light sources; the image acquisition part is arranged at a central position of the carrying part, and the plurality of light sources are arranged on the carrying part around the image acquisition part; the image acquisition part can perform image acquisition on a target object illuminated by the plurality of light sources at a plurality of acquisition viewing angles, and the plurality of light sources can illuminate the target object under a plurality of lighting conditions; wherein, for any acquisition viewing angle, the image acquisition part acquires a plurality of original images of the target object under different lighting conditions, and the plurality of original images acquired at each acquisition viewing angle are used to determine a target PBR material map of the target object at the acquisition viewing angle.

[0014] In a possible implementation, the device also includes a control unit, which is used to control the turning on and off of the multiple light sources and at least one of the lighting conditions, so that the multiple light sources can illuminate the target object under multiple lighting conditions; the control unit is also used to control the acquisition angle of the image acquisition unit, and control the image acquisition unit to acquire at least one original image of the target object under each lighting condition at any acquisition angle.

[0015] In a possible implementation, the device further includes a computing unit, and the computing unit is configured to execute the above method.

[0016] According to one aspect of the present disclosure, an electronic device is provided, comprising: a processor; and a memory for storing processor-executable instructions; wherein the processor is configured to call the instructions stored in the memory to execute the above method.

[0017] According to one aspect of the present disclosure, a computer-readable storage medium is provided, on which computer program instructions are stored, and the computer program instructions implement the above method when executed by a processor.

[0018] According to one aspect of the present disclosure, a computer program product is provided, including a computer-readable code, or a non-volatile computer-readable storage medium carrying the computer-readable code; when the computer-readable code runs in a processor of an electronic device, the processor in the electronic device executes the above method.

[0019] In the disclosed embodiment, multiple original images of different lighting conditions corresponding to the target object are collected at the target perspective, and a PBR material map prediction model is used to perform multi-stage map prediction. The target PBR material map with the same resolution as the original image can be quickly output end-to-end, thereby effectively improving the material scanning efficiency of the target object at the target perspective.

[0020] It should be understood that the above general description and the following detailed description are exemplary and explanatory only and do not limit the present disclosure. Other features and aspects of the present disclosure will become clear from the following detailed description of exemplary embodiments with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The drawings herein are incorporated into the specification and constitute a part of the specification. These drawings illustrate embodiments consistent with the present disclosure and are used to illustrate the technical solutions of the present disclosure together with the specification.

[0022] Figure 1 A flow chart of a PBR material map generation method according to an embodiment of the present disclosure is shown.

[0023] Figure 2 A schematic diagram showing pixel-by-pixel texture prediction of a plurality of images to be processed using a PBR material texture prediction model according to an embodiment of the present disclosure.

[0024] Figure 3 A flow chart of a three-dimensional object material scanning method according to an embodiment of the present disclosure is shown.

[0025] Figure 4 A block diagram of a PBR material map generating device according to an embodiment of the present disclosure is shown.

[0026] Figure 5 A schematic diagram of a three-dimensional object material scanning device according to an embodiment of the present disclosure is shown.

[0027] Figure 6 A block diagram of an electronic device according to an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0028] Various exemplary embodiments, features and aspects of the present disclosure will be described in detail below with reference to the accompanying drawings. The same reference numerals in the accompanying drawings represent elements with the same or similar functions. Although various aspects of the embodiments are shown in the accompanying drawings, the drawings are not necessarily drawn to scale unless otherwise specified.

[0029] The word “exemplary” is used exclusively herein to mean “serving as an example, example, or illustration.” Any embodiment described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments.

[0030] The term "and / or" herein is only a description of the association relationship of the associated objects, indicating that there may be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the term "at least one" herein represents any combination of at least two of any one or more of a plurality of. For example, including at least one of A, B, and C can represent including any one or more elements selected from the set consisting of A, B, and C.

[0031] In addition, in order to better illustrate the present disclosure, numerous specific details are given in the following specific embodiments. It should be understood by those skilled in the art that the present disclosure can also be implemented without certain specific details. In some examples, methods, means, components and circuits well known to those skilled in the art are not described in detail in order to highlight the subject matter of the present disclosure.

[0032] Physically Based Rendering (PBR) material is a rendering technology widely used in the field of computer graphics. PBR material aims to simulate the behavior of real-world lighting based on the principles of physics, so as to present the lighting and surface details of the material more realistically. It uses physically based parameters (such as reflectivity, roughness, metalness, etc.) to describe the optical properties of the material.

[0033] Traditional PBR material scanning solutions are based on the Lambert model assumption, but the Lambert model assumes that the surface of an object is smooth and has only diffuse reflections, which results in the PBR material scanning solution based on the Lambert model not being able to correctly handle objects with high light reflections. In addition, traditional PBR material scanning solutions usually require the direction of the light source to be calibrated, which can cause great trouble in practical applications.

[0034] The embodiment of the present disclosure provides a PBR material map generation method, which can support efficient PBR material scanning of any object in any lighting environment. The PBR material map generation method provided by the embodiment of the present disclosure is described in detail below.

[0035] Figure 1 A flowchart of a method for generating a PBR material map according to an embodiment of the present disclosure is shown. The method can be executed by an electronic device such as a terminal device or a server. The terminal device can be a user equipment (UE), a mobile device, a user terminal, a terminal, a cellular phone, a cordless phone, a personal digital assistant (PDA), a handheld device, a computing device, a vehicle-mounted device, a wearable device, etc. The method can be implemented by a processor calling a computer-readable instruction stored in a memory. Alternatively, the method can be executed by a server. Figure 1 As shown, the method includes:

[0036] In step S11, a plurality of original images of the target object under different lighting conditions are collected at a target viewing angle, wherein the plurality of original images have a target resolution.

[0037] The target viewing angle may be any single viewing angle that can capture the target object, and the present disclosure does not make any specific limitation on this.

[0038] Different lighting conditions may include different lighting directions and different lighting intensities. The lighting directions may be the same but the lighting intensities are different, the lighting intensities may be the same but the lighting directions are different, or both may be different. The lighting direction and lighting intensity under each lighting condition may be flexibly set according to actual application needs, and the present disclosure does not specifically limit this.

[0039] In one example, the image acquisition module (e.g., a camera) remains stationary, the current acquisition angle of view is the target acquisition angle of view, and the target object is illuminated using different directional conditions. Then, the image acquisition module acquires multiple original images of different lighting conditions corresponding to the target object under the target angle of view.

[0040] The specific number of the multiple original images can be flexibly set according to actual conditions. For example, four original images of different lighting conditions corresponding to the target object are collected under the target viewing angle. The present disclosure does not make specific limitations on this.

[0041] The specific size of the target resolution depends on the hardware properties of the image acquisition module, and the present disclosure does not make any specific limitation on this.

[0042] In step S12, a PBR material map prediction model is used to perform multi-stage map prediction based on multiple original images to determine a target PBR material map of the target object at a target viewing angle, wherein the target PBR material map has a target resolution.

[0043] SDM-UniPS (a groundbreaking scalable, detailed, mask-free and universal photometric stereo network) in related technologies can support material scanning under general lighting environments, but it has a strong resolution limit for the generated PBR material maps, and its accuracy is significantly reduced when the resolution is higher than 2K. This is mainly because: (1) SDM-UniPS is limited by the label (token) scale of the model and can only adjust the input original image to a PBR material map of a fixed resolution and then upsample it to the original resolution; (2) SDM-UniPS uses pooling to fuse image features under different lighting conditions, which cannot achieve pixel-level feature fusion and will lose some high-frequency details.

[0044] In the disclosed embodiment, millions of training data are constructed based on the existing 3D asset library, covering more than 1k high-dynamic range (HDR) environment lighting, more than 20,000 3D assets, and more than 200,000 real materials. Furthermore, a PBR material map prediction model is trained that can perform PBR material scanning for all objects in a general lighting environment, and can support the scanning and generation of PBR material maps of any resolution in any lighting environment.

[0045] Based on multiple original images of the target object under different lighting conditions collected from the target perspective, the PBR material map prediction model is used to perform multi-stage map prediction, and the target PBR material map with the same resolution as the original image is quickly output end-to-end, effectively realizing the material scanning of the target object under the target perspective.

[0046] In the disclosed embodiment, multiple original images of different lighting conditions corresponding to the target object are collected at the target perspective, and a PBR material map prediction model is used to perform multi-stage map prediction. The target PBR material map with the same resolution as the original image can be quickly output end-to-end, thereby effectively improving the material scanning efficiency of the target object at the target perspective.

[0047] In a possible implementation, a PBR material map prediction model is used to perform multi-stage map prediction based on multiple original images to determine a target PBR material map of the target object at a target perspective, including: for the j-th stage map prediction, based on multiple original images, a plurality of to-be-processed images corresponding to the j-th stage map prediction are determined, wherein j is a positive integer greater than or equal to 1; a PBR material map prediction model is used to perform pixel-by-pixel map prediction on the plurality of to-be-processed images corresponding to the j-th stage map prediction to obtain the j-th stage PBR material map; when the resolution of the j-th stage PBR material map reaches the target resolution, the j-th stage PBR material map is determined as the target PBR material map.

[0048] The PBR material map prediction model performs map prediction pixel by pixel during the process of executing map prediction at each stage, so that when predicting the map at each stage, it can output a PBR material map with the same resolution as the input multiple images to be processed.

[0049] In one example, the PBR material mapping prediction model uses the set transformer network structure in deep learning to achieve pixel-by-pixel mapping prediction for multiple input images to be processed.

[0050] Figure 2 A schematic diagram showing pixel-by-pixel texture prediction of a plurality of images to be processed using a PBR material texture prediction model according to an embodiment of the present disclosure.

[0051] like Figure 2 As shown, multiple images to be processed are input into the PBR material map prediction model, and each image to be processed passes through a pyramid-structured visual transformation network (Pyramid Vision Transformer) to extract pixel-level image features (latent space representation) to obtain the first feature map corresponding to each image to be processed, wherein the first feature map has the same resolution as the image to be processed.

[0052] The feature values ​​at the same pixel position in each first feature map are combined into a first feature vector. For the first feature vector at each pixel position, the first feature vector is passed through a set attention block (Set AttentionBlocks) to achieve feature exchange between different first feature maps, and a second feature vector at each pixel position is obtained. Then, the feature value in the second feature vector at each pixel position is mapped back to the corresponding pixel position in each first feature map. After the above processing, multiple first feature maps are updated to multiple second feature maps.

[0053] In addition, the first feature vector of each pixel position will pass through a pooling multi-head attention network. Compared with the traditional pooling operation, the attention operation here increases the interactive fusion of information to obtain the global features of different first feature maps. After the above processing, a new second feature map is obtained.

[0054] The above-mentioned multiple second feature maps are passed through a fully connected layer to obtain a PBR material map with the same resolution as the input multiple images to be processed.

[0055] The PBR material mapping prediction model can be executed during the mapping prediction process at each stage. Figure 2 The texture prediction is performed pixel by pixel as shown. For multiple original images collected under different lighting conditions at a certain target viewing angle, a PBR texture map corresponding to the target viewing angle can be obtained through processing by the PBR texture map prediction model.

[0056] In one possible implementation, the resolution of multiple images to be processed corresponding to the j-th stage texture prediction is greater than the resolution of multiple images to be processed corresponding to the j-1-th stage texture prediction, and is less than or equal to the target resolution; the resolution of the j-th stage PBR material map is equal to the resolution of the multiple images to be processed corresponding to the j-th stage texture prediction.

[0057] When the PBR material map prediction model performs map prediction at any stage, a PBR material map with the same resolution as the multiple images to be processed input at that stage can be obtained.

[0058] In the process of multi-stage texture prediction by the PBR material map prediction model, the resolution of the PBR material map output at each stage is gradually improved by gradually increasing the resolution of the multiple images to be processed input at each stage, so as to effectively obtain the target PBR material map with the same target resolution as the original image.

[0059] In a possible implementation, the PBR material map prediction model includes: a first prediction sub-model and a second prediction sub-model; based on multiple original images, the PBR material map prediction model is used to perform multi-stage map prediction to determine the target PBR material map of the target object under the target perspective, including: based on the multiple original images, the first prediction sub-model is used to perform the first stage map prediction to obtain the first stage PBR material map; based on the multiple original images and the i-1 stage PBR material map, the second prediction sub-model is used to perform the i-th stage map prediction to obtain the i-th stage PBR material map, wherein i is a positive integer greater than 1; when the resolution of the i-th stage PBR material map reaches the target resolution, the i-th stage PBR material map is determined as the target PBR material map.

[0060] The PBR material mapping prediction model can include two parts: a first prediction sub-model and a second prediction sub-model. The first prediction sub-model and the second prediction sub-model can both execute Figure 2 The pixel-by-pixel texture prediction shown outputs a PBR material map.

[0061] The first prediction sub-model only performs the first stage texture prediction to obtain the first stage PBR material map. The second prediction sub-model performs subsequent multi-stage texture prediction, and at each stage, the resolution of the PBR material map generated in the previous stage is increased until a target PBR material map with the same resolution as the original image is obtained. When the second prediction sub-model performs subsequent multi-stage texture prediction, it uses the original image and the output of the second prediction sub-model in the previous stage (i.e., the i-1th stage PBR material map), and gradually iterates to increase the resolution until a PBR material map of the target resolution is obtained.

[0062] In a possible implementation, a first prediction sub-model is used to perform a first stage texture prediction based on multiple original images to obtain a first stage PBR material map, including: performing downsampling processing on the multiple original images to obtain multiple images to be processed corresponding to the first stage texture prediction, wherein the resolution of the multiple images to be processed corresponding to the first stage texture prediction is less than the target resolution; the multiple images to be processed corresponding to the first stage texture prediction are input into the first prediction sub-model to perform a first stage texture prediction to obtain the first stage PBR material map.

[0063] The input of the first prediction sub-model is a plurality of to-be-processed images corresponding to the first-stage texture prediction obtained by performing downsampling processing on a plurality of original images.

[0064] The resolution n×n of the multiple images to be processed corresponding to the first stage map prediction is less than the target resolution of the original image. The specific value of the resolution n×n can be flexibly set according to the actual scene, for example, n×n=32×32, and the present disclosure does not make specific limitations on this.

[0065] The first prediction sub-model performs the following operations on multiple images to be processed with a resolution of n×n: Figure 2 The pixel-by-pixel texture prediction shown above results in a first-stage PBR material map with a resolution of n×n.

[0066] In a possible implementation, according to multiple original images and the i-1 stage PBR material map, the second prediction sub-model is used to perform i-stage texture prediction to obtain the i-stage PBR material map, including: performing downsampling processing on the multiple original images to obtain multiple images to be processed corresponding to the i-stage texture prediction, wherein the resolution of the multiple images to be processed corresponding to the i-stage texture prediction is greater than the resolution of the multiple images to be processed corresponding to the i-1 stage texture prediction, and is less than or equal to the target resolution; performing upsampling processing on the i-1 stage PBR material map to obtain the PBR material map to be processed corresponding to the i-stage texture prediction, wherein the PBR material map to be processed corresponding to the i-stage texture prediction has the same resolution as the multiple images to be processed corresponding to the i-stage texture prediction; the multiple images to be processed corresponding to the i-stage texture prediction and the PBR material map to be processed corresponding to the i-stage texture prediction are input into the second prediction sub-model to perform i-stage texture prediction to obtain the i-stage PBR material map.

[0067] When the second prediction sub-model performs the i-th stage texture prediction, the input includes two parts: one part is the multiple images to be processed corresponding to the i-th stage texture prediction obtained by downsampling multiple original images; the other part is the PBR material map to be processed corresponding to the i-th stage texture prediction obtained by upsampling the i-1-th stage PBR material map. For example, if there are m original images, then in each i-th stage after the first stage, the input to the second prediction sub-model is the m images to be processed obtained by downsampling the m original images, and the one PBR material map to be processed obtained by upsampling the i-1-th stage PBR material map. These m+1 images are used as the input of the second prediction sub-model together. Figure 2 The pixel-by-pixel texture prediction shown in FIG. 1 obtains a PBR material map of the i-th stage as the output of the second prediction sub-model.

[0068] The resolution of the multiple images to be processed corresponding to the i-th stage texture prediction is greater than the resolution of the multiple images to be processed corresponding to the i-1-th stage texture prediction, and is less than or equal to the target resolution. In one example, the resolution of the multiple images to be processed corresponding to the i-th stage texture prediction is (n×2 i-1 )×(n×2 i-1 The resolution of the PBR material map to be processed corresponding to the i-th stage texture prediction is also (n×2 i-1 )×(n×2 i-1 ).

[0069] The second prediction sub-model predicts the corresponding resolution (n×2 i-1 )×(n×2 i-1 ) and the PBR material maps to be processed, and execute Figure 2 The pixel-by-pixel texture prediction shown above yields a resolution of (n×2 i-1 )×(n×2 i-1 )’s i-th stage PBR material map.

[0070] The second prediction sub-model iteratively performs multi-stage texture prediction until a target PBR material map with the same resolution as the original image is obtained.

[0071] In one example, multiple original images with a resolution of 256×256 corresponding to different lighting conditions of the target object are collected at a target viewing angle;

[0072] Down-sampling multiple original images with a resolution of 256×256 to obtain multiple to-be-processed images with a resolution of 32×32 corresponding to the first-stage texture prediction, inputting the multiple to-be-processed images with a resolution of 32×32 into the first prediction sub-model for first-stage texture prediction, and obtaining the first-stage PBR material map with a resolution of 32×32;

[0073] Down-sampling a plurality of original images with a resolution of 256×256 to obtain a plurality of images to be processed with a resolution of 64×64 corresponding to the second-stage texture prediction, and up-sampling the first-stage PBR material map with a resolution of 32×32 to obtain a PBR material map to be processed with a resolution of 64×64 corresponding to the second-stage texture prediction, and inputting the plurality of images to be processed with a resolution of 64×64 and the PBR material map to be processed into the second prediction sub-model for second-stage texture prediction to obtain a second-stage PBR material map with a resolution of 64×64;

[0074] Down-sampling a plurality of original images with a resolution of 256×256 to obtain a plurality of images to be processed with a resolution of 128×128 corresponding to the third-stage texture prediction, and up-sampling the second-stage PBR material map with a resolution of 64×64 to obtain a PBR material map to be processed with a resolution of 128×128 corresponding to the third-stage texture prediction, and inputting the plurality of images to be processed with a resolution of 128×128 and the PBR material map to be processed into the second prediction sub-model for third-stage texture prediction to obtain a third-stage PBR material map with a resolution of 128×128;

[0075] A plurality of original images with a resolution of 256×256 are downsampled to obtain a plurality of images to be processed with a resolution of 256×256 corresponding to the fourth-stage texture prediction, and a third-stage PBR material map with a resolution of 128×128 is upsampled to obtain a PBR material map to be processed with a resolution of 256×256 corresponding to the fourth-stage texture prediction, and the plurality of images to be processed with a resolution of 256×256 and the PBR material map to be processed are input into the second prediction sub-model for fourth-stage texture prediction to obtain a target PBR material map with a resolution of 256×256.

[0076] In a possible implementation, based on multiple original images and the i-1th stage PBR material map, the second prediction sub-model is used to perform i-stage map prediction to obtain the i-stage PBR material map, including: fusing the multiple original images and the i-1th stage PBR material map to obtain a fused image; sampling the fused image to obtain a to-be-processed image corresponding to the i-stage map prediction, wherein the resolution of the to-be-processed image corresponding to the i-stage map prediction is greater than the resolution of the to-be-processed image corresponding to the i-1th stage map prediction, and is less than or equal to the target resolution; the to-be-processed image corresponding to the i-stage map prediction is input into the second prediction sub-model to perform i-stage map prediction to obtain the i-stage PBR material map.

[0077] In order to improve the model processing efficiency of the i-th stage texture prediction, the post-fusion sampling processing can be performed on multiple original images and the i-1-th stage PBR material map in advance, and only one image to be processed corresponding to the i-th stage texture prediction is obtained. Then, the one image to be processed is input into the second prediction sub-model for the i-th stage texture prediction to quickly obtain the i-th stage PBR material map. The present disclosure does not limit the specific method of performing fusion on multiple original images and the i-1-th stage PBR material map. Upsampling or downsampling of the fused image can be determined according to the resolution of the fused image, the resolution of the image to be processed corresponding to the i-1-th stage texture prediction, and the target resolution, so as to obtain an image to be processed corresponding to the i-th stage texture prediction with a resolution between the resolution of the image to be processed corresponding to the i-1-th stage texture prediction and the target resolution, so as to achieve a gradual improvement in resolution through iteration.

[0078] In a possible implementation, a PBR material map prediction model is used to perform multi-stage map prediction based on multiple original images to determine the target PBR material map of the target object at the target perspective, including: when the target resolution is higher than the resolution threshold corresponding to the PBR material map prediction model, each original image is split into image blocks to obtain multiple image block groups, wherein the multiple image blocks included in each image block group correspond to the same position area in the multiple original images, and the resolution of the multiple image blocks included in each image block group is less than or equal to the resolution threshold; based on the multiple image blocks included in each image block group, a PBR material map prediction model is used to perform multi-stage map prediction to determine the PBR material map block corresponding to each image block group; and the PBR material map blocks corresponding to the multiple image block groups are merged to obtain the target PBR material map.

[0079] Due to the limitation of hardware processing performance, the PBR texture prediction model may have a resolution threshold. For example, the PBR texture prediction model uses a graphics processing unit (GPU) for image processing. The higher the resolution, the larger the video memory required. However, the GPU video memory is fixed. If the PBR texture prediction model processes a high-resolution image that exceeds the resolution threshold, the GPU video memory will overflow, resulting in reduced output accuracy of the PBR texture prediction model.

[0080] The specific value of the resolution threshold corresponding to the PBR material map prediction model can be flexibly set according to the hardware processing performance in the actual application scenario, and this disclosure does not make any specific limitations on this.

[0081] When the hardware performance of the image acquisition module is high and the target resolution of the multiple original images acquired is higher than the resolution threshold corresponding to the PBR material map prediction model, in order to ensure the output accuracy of the model, each original image can be split into image blocks (patch) to obtain multiple image block groups, and the multiple image blocks included in each image block group correspond to the same position area in multiple original images.

[0082] Taking the image block group as a unit, the PBR texture map prediction model is used to perform multi-stage texture prediction to determine the PBR texture map block corresponding to each image block group. Since the resolution of multiple image blocks included in each image block group is less than or equal to the resolution threshold, it can ensure that the PBR texture map block corresponding to each image block group has a high accuracy.

[0083] After obtaining the PBR texture map blocks corresponding to the multiple image block groups, the PBR texture map blocks corresponding to the multiple image block groups can be fused to achieve the splicing of the multiple PBR texture map blocks and obtain a target PBR texture map with the same target resolution as the original image.

[0084] In one example, PBR material map blocks corresponding to multiple image block groups are fused to obtain a target PBR material map, including: Gaussian fusion of the PBR material map blocks corresponding to the multiple image block groups to obtain the target PBR material map.

[0085] Through Gaussian fusion, the seamless splicing of multiple PBR material map blocks can be effectively achieved, and the image quality of the target PBR material map can be improved.

[0086] Based on the above-mentioned patch splitting and Gaussian fusion methods, the disclosed embodiment can utilize the PBR material map prediction model to support PBR material maps of any resolution, effectively meeting the resolution requirements of various practical application scenarios.

[0087] In a possible implementation, the target PBR material map includes: a color map, a normal map, a roughness map, and a metalness map.

[0088] The target PBR material maps include: color map, normal map, roughness map, and metalness map, which can be directly applied to various rendering engines according to the actual application scenario requirements.

[0089] The disclosed embodiment provides a PBR material map prediction model that can perform PBR material scanning for all objects in a universal lighting environment, and can support the scanning and generation of PBR material maps of any resolution in any lighting environment. Using the PBR material map prediction model, a multi-stage map prediction is performed on multiple original images of different lighting conditions corresponding to the target object collected under the target perspective, and a target PBR material map with the same resolution as the original image can be quickly output end-to-end, thereby effectively improving the material scanning efficiency of the target object under the target perspective.

[0090] The structured light scanning method in the prior art can only obtain the three-dimensional structure of the object, but cannot realize material scanning. In addition, the photometric stereo scanning method in the prior art uses the principle of photometric stereo to realize PBR material scanning, but it has high requirements on the light source, needs to maintain a dark room environment, and needs to build a spherical LED lighting environment to ensure that the light source covers the object from all angles, which makes the entire scanning equipment quite large and cannot be portable.

[0091] Based on the above PBR material map generation method, the embodiment of the present disclosure also provides a 3D object material scanning method, which can realize portable scanning of 3D object structure and material under general lighting environment. The 3D object material scanning method provided by the embodiment of the present disclosure is described in detail below.

[0092] Figure 3 FIG. 1 is a flow chart of a method for scanning a three-dimensional object material according to an embodiment of the present disclosure. The method can be applied to a three-dimensional object material scanning device. Figure 3 As shown, the method includes:

[0093] In step S31, a plurality of original images corresponding to the target object captured at a plurality of capture viewing angles and under different lighting conditions are acquired, wherein the plurality of capture viewing angles can surround and cover the target object, and the plurality of original images captured at each capture viewing angle have a target resolution.

[0094] The three-dimensional object material scanning device includes an image acquisition unit. The image acquisition unit is used to surround the target object and acquire multiple original images of the target object under different lighting conditions at multiple acquisition viewing angles.

[0095] In one example, an image acquisition unit (e.g., a camera) moves to a certain acquisition viewing angle, uses different lighting directions and different lighting intensities to illuminate a target object, and constructs a plurality of different lighting conditions. Then, the image acquisition unit acquires a plurality of original images of different lighting conditions corresponding to the target object at the acquisition viewing angle.

[0096] In one example, in order to completely surround and cover the target object, there is a preset percentage overlap range between two adjacent acquisition viewing angles. The specific value of the preset percentage can be flexibly set according to actual conditions, for example, the preset percentage is 30%, and the embodiment of the present disclosure does not specifically limit this.

[0097] The specific number of multiple original images under different lighting conditions collected at each acquisition perspective can be flexibly set according to actual conditions. For example, 4 original images under different lighting conditions corresponding to the target object are collected at each acquisition perspective, and the present disclosure does not make specific limitations on this.

[0098] In one example, a three-dimensional object material scanning device includes: a plurality of light sources arranged around an image acquisition unit, wherein the plurality of light sources are used to illuminate a target object under different lighting conditions; and an image acquisition submodule is used to acquire an image of the target object.

[0099] In step S32, based on the multiple original images collected at each acquisition perspective, a PBR material map prediction model is used to perform multi-stage map prediction to determine the target PBR material map of the target object at each acquisition perspective, wherein the target PBR material map at each acquisition perspective has a target resolution.

[0100] The PBR material mapping prediction model is used to perform multi-stage mapping prediction on multiple original images collected at each acquisition perspective to determine the target PBR material mapping of the target object at each acquisition perspective.

[0101] The specific process of the PBR material mapping prediction model performing multi-stage mapping prediction on multiple original images collected at each acquisition perspective can be referred to above. Figure 1 and / or Figure 2 The specific process of the PBR material mapping prediction model in the relevant embodiment performing multi-stage mapping prediction on multiple original images collected under the target acquisition perspective will not be described in detail here.

[0102] In step S33, the target object is three-dimensionally reconstructed based on the multiple original images collected at each acquisition viewing angle to obtain an initial three-dimensional model corresponding to the target object.

[0103] In one example, multiple original images captured at each capture perspective are cut out to retain only the target object in the image, thereby obtaining multiple processed images; and the multiple processed images are three-dimensionally reconstructed to obtain an initial three-dimensional model corresponding to the target object.

[0104] The specific process of three-dimensional reconstruction of the target object can refer to the relevant technology, and the present disclosure does not make any specific limitation on this.

[0105] In step S34, material mapping is performed on the initial three-dimensional model according to the target PBR material mapping of the target object at each acquisition viewing angle to obtain a target three-dimensional model corresponding to the target object.

[0106] Since the initial three-dimensional model corresponding to the target object obtained after three-dimensional reconstruction can only reflect the three-dimensional structure of the target object, the initial three-dimensional model is textured according to the target PBR material map of the target object at each acquisition perspective obtained above, so as to obtain a target three-dimensional model that can reflect both the three-dimensional structure of the target object and the PBR material of the target object.

[0107] The three-dimensional object material scanning method of the disclosed embodiment can realize portable scanning of three-dimensional object structure and material in a general lighting environment, and further can realize a three-dimensional asset scanning solution in any lighting environment, greatly facilitating asset production in the virtual shooting process.

[0108] It can be understood that the above-mentioned various method embodiments mentioned in the present disclosure can be combined with each other to form a combined embodiment without violating the principle logic. Due to space limitations, the present disclosure will not repeat them. It can be understood by those skilled in the art that in the above-mentioned method of the specific implementation method, the specific execution order of each step should be determined according to its function and possible internal logic.

[0109] In addition, the present disclosure also provides a PBR material map generation device, a three-dimensional object material scanning device, an electronic device, a computer-readable storage medium, and a program, all of which can be used to implement any PBR material map generation and three-dimensional object material scanning device method provided by the present disclosure. The corresponding technical solutions and descriptions are referred to the corresponding records in the method part and will not be repeated here.

[0110] Figure 4 A block diagram of a PBR material map generation device according to an embodiment of the present disclosure is shown. Figure 4 As shown, the device 40 includes:

[0111] An image acquisition module 41 is used to acquire a plurality of original images of a target object under different illumination conditions at a target viewing angle, wherein the plurality of original images have a target resolution;

[0112] The PBR material map generation module 42 is used to perform multi-stage map prediction based on multiple original images using a PBR material map prediction model to determine a target PBR material map of the target object at a target viewing angle, wherein the target PBR material map has a target resolution.

[0113] In a possible implementation, the PBR material map generation module 42 is specifically used to:

[0114] For the j-stage texture prediction, determining a plurality of to-be-processed images corresponding to the j-stage texture prediction according to the plurality of original images, wherein j is a positive integer greater than or equal to 1;

[0115] Using the PBR material map prediction model, a pixel-by-pixel map prediction is performed on a plurality of to-be-processed images corresponding to the j-stage map prediction to obtain the j-stage PBR material map;

[0116] When the resolution of the j-th stage PBR material map reaches the target resolution, the j-th stage PBR material map is determined as the target PBR material map.

[0117] In a possible implementation, the resolution of the multiple images to be processed corresponding to the j-th stage texture prediction is greater than the resolution of the multiple images to be processed corresponding to the j-1-th stage texture prediction, and is less than or equal to the target resolution;

[0118] The resolution of the j-th stage PBR material map is equal to the resolution of the multiple images to be processed corresponding to the j-th stage map prediction.

[0119] In a possible implementation, the PBR material mapping prediction model includes: a first prediction sub-model and a second prediction sub-model;

[0120] The PBR material map generation module 42 is specifically used for:

[0121] According to the multiple original images, the first prediction sub-model is used to perform the first stage texture prediction to obtain the first stage PBR material texture;

[0122] According to the plurality of original images and the i-1th stage PBR material map, the second prediction sub-model is used to perform the i-th stage map prediction to obtain the i-th stage PBR material map, where i is a positive integer greater than 1;

[0123] When the resolution of the PBR material map at the i-th stage reaches the target resolution, the PBR material map at the i-th stage is determined as the target PBR material map.

[0124] In a possible implementation, the PBR material map generation module 42 is specifically used to:

[0125] Performing downsampling processing on the multiple original images to obtain multiple to-be-processed images corresponding to the first-stage texture prediction, wherein the resolution of the multiple to-be-processed images corresponding to the first-stage texture prediction is less than the target resolution;

[0126] The multiple images to be processed corresponding to the first stage texture prediction are input into the first prediction sub-model to perform the first stage texture prediction to obtain the first stage PBR material map.

[0127] In a possible implementation, the PBR material map generation module 42 is specifically used to:

[0128] Performing downsampling processing on the multiple original images to obtain multiple to-be-processed images corresponding to the i-th stage texture prediction, wherein the resolution of the multiple to-be-processed images corresponding to the i-th stage texture prediction is greater than the resolution of the multiple to-be-processed images corresponding to the i-1-th stage texture prediction, and is less than or equal to the target resolution;

[0129] Perform upsampling processing on the PBR material map of the i-1th stage to obtain a PBR material map to be processed corresponding to the i-th stage map prediction, wherein the PBR material map to be processed corresponding to the i-th stage map prediction and the multiple images to be processed corresponding to the i-th stage map prediction have the same resolution;

[0130] The multiple images to be processed corresponding to the i-th stage texture prediction and the PBR material map to be processed corresponding to the i-th stage texture prediction are input into the second prediction sub-model to perform the i-th stage texture prediction to obtain the i-th stage PBR material map.

[0131] In a possible implementation, the PBR material map generation module 42 is specifically used to:

[0132] Fusing multiple original images and the i-1th stage PBR material map to obtain a fused image;

[0133] Sampling the fused image to obtain an image to be processed corresponding to the i-th stage texture prediction, wherein the resolution of the image to be processed corresponding to the i-th stage texture prediction is greater than the resolution of the image to be processed corresponding to the i-1-th stage texture prediction, and is less than or equal to the target resolution;

[0134] The image to be processed corresponding to the i-th stage texture prediction is input into the second prediction sub-model to perform the i-th stage texture prediction to obtain the i-th stage PBR material map.

[0135] In a possible implementation, the PBR material map generation module 42 is specifically used to:

[0136] When the target resolution is higher than the resolution threshold corresponding to the PBR material map prediction model, each original image is split into image blocks to obtain multiple image block groups, wherein the multiple image blocks included in each image block group correspond to the same position area in the multiple original images, and the resolution of the multiple image blocks included in each image block group is less than or equal to the resolution threshold;

[0137] According to the multiple image blocks included in each image block group, a PBR material map prediction model is used to perform multi-stage map prediction to determine the PBR material map block corresponding to each image block group;

[0138] The PBR material map blocks corresponding to multiple image block groups are merged to obtain the target PBR material map.

[0139] Figure 5 A schematic diagram of a three-dimensional object material scanning device according to an embodiment of the present disclosure is shown. Figure 5 As shown, the device 50 includes: a carrying part 51, an image acquisition part 52 and a plurality of light sources 53; the image acquisition part 52 is arranged at the center of the carrying part 51, and the plurality of light sources 53 are arranged on the carrying part 51 around the image acquisition part 52;

[0140] The image acquisition unit 52 can acquire images of a target object illuminated by a plurality of light sources 53 at a plurality of acquisition viewing angles, and the plurality of light sources 53 can illuminate the target object under a plurality of illumination conditions;

[0141] Among them, for any acquisition viewing angle, the image acquisition unit 52 acquires multiple original images of the target object under different lighting conditions, and the multiple original images acquired at each acquisition viewing angle are used to determine the target PBR material map of the target object at the acquisition viewing angle.

[0142] In one example, a three-dimensional object material scanning device includes: a camera (image acquisition unit 52), 8 LED spotlights (multiple light sources 53), a panel (carrying unit 51), a camera bracket 54 (for fixing and supporting the image acquisition unit), a power cord 55 (for supplying power to the image acquisition unit, the light source, etc.), etc. The panel is cut according to a regular octagon inscribed in a circle; each LED spotlight is installed at the vertex of the regular octagon, and each LED spotlight is fixed to the panel by a universal shaft, and the direction can be freely adjusted; the camera bracket is fixed to the back of the panel by letter screws, so that the camera lens is kept at the center of the panel, and the camera and the camera bracket can be rotatably connected to change the camera's acquisition angle of view.

[0143] The specific forms of the image acquisition unit and multiple light sources, the connection method and connection position between the multiple light sources and the supporting unit, the material and shape of the supporting unit, and the specific number of the multiple light sources can be flexibly set according to actual conditions, and the present disclosure does not make specific limitations on this.

[0144] The device is simple and portable, and can construct a variety of lighting conditions including different lighting directions and different lighting intensities by controlling the light source. The image acquisition unit can collect images of target objects under various lighting conditions at different acquisition angles to obtain high-precision target PBR material maps, thereby improving the convenience and accuracy of material map acquisition.

[0145] In one possible implementation, the device 50 also includes a control unit, which is used to control the turning on and off of the multiple light sources 53 and at least one of the lighting conditions, so that the multiple light sources 53 can illuminate the target object under multiple lighting conditions; the control unit is also used to control the acquisition angle of the image acquisition unit 52, and control the image acquisition unit 52 to capture at least one original image of the target object under each lighting condition at any acquisition angle.

[0146] For example, the control unit can send a signal to control and adjust the image acquisition unit's acquisition angle of view of the target object, and send a signal to adjust the illumination conditions (including illumination direction and / or illumination intensity) of the target object by multiple light sources at the acquisition angle, and then send a signal to control the image acquisition unit to perform image acquisition under different illumination conditions of the target object at the acquisition angle. The control unit can adjust the illumination conditions formed by the multiple light sources by adjusting the opening, closing, direction (illumination direction), and intensity (illumination intensity) of each of the multiple light sources 53, and can flexibly construct various required illumination conditions. For example, the control unit can send a signal to control multiple light sources to light up in sequence, thereby constructing different illumination conditions. Taking the example of 8 LED spotlights in the above text as an example, after the 8 LED spotlights are set according to the preset direction, the control unit can control each of the 8 LED spotlights to light up in turn (lighting up one LED spotlight at a time), thereby forming 8 lighting directions, and then the light intensity of each LED spotlight can be controlled to construct the current lighting condition. The image acquisition unit can collect the original image under each lighting condition at any acquisition viewing angle to obtain the target PBR material map.

[0147] In one example, the control unit includes a transformer, an R3 development board, a relay, a switch button, and a device interface (for example, a control unit power interface, an image acquisition unit switch interface, multiple light source power interfaces, and multiple light source power supply interfaces). In addition to the above components, the control unit may also include other necessary components according to actual application requirements, and the present disclosure does not specifically limit this.

[0148] In a possible implementation, the device 50 further includes a computing unit, which is used to execute the above method embodiment.

[0149] In one example, the computing unit may be a computing device. Figure 3In the three-dimensional object material scanning method shown: first, using the calculation unit, based on multiple original images collected at each acquisition perspective and the PBR material map prediction model, multi-stage map prediction is performed to determine the target PBR material map of the target object at each acquisition perspective; and, using the calculation unit, based on the multiple original images collected at each acquisition perspective, the target object is three-dimensionally reconstructed to obtain an initial three-dimensional model corresponding to the target object; finally, using the calculation unit, based on the target PBR material map of the target object at each acquisition perspective, material mapping is performed on the initial three-dimensional model to obtain a target three-dimensional model corresponding to the target object.

[0150] In one example, the computing unit may be a plurality of computing devices, including: a PBR material mapping prediction device, a 3D reconstruction device, and a mapping device; in the above Figure 3 In the three-dimensional object material scanning method shown: first, a PBR material map prediction device is used to perform multi-stage map prediction based on multiple original images collected at each acquisition perspective and a PBR material map prediction model to determine the target PBR material map of the target object at each acquisition perspective; and a three-dimensional reconstruction device is used to perform three-dimensional reconstruction of the target object based on multiple original images collected at each acquisition perspective to obtain an initial three-dimensional model corresponding to the target object; finally, a mapping device is used to perform material mapping on the initial three-dimensional model based on the target PBR material map of the target object at each acquisition perspective to obtain a target three-dimensional model corresponding to the target object.

[0151] This method has a specific technical connection with the internal structure of the computer system, and can solve the technical problem of how to improve the hardware computing efficiency or execution effect (including reducing the amount of data storage, reducing the amount of data transmission, increasing the hardware processing speed, etc.), thereby obtaining the technical effect of improving the internal performance of the computer system in accordance with the laws of nature.

[0152] In some embodiments, the functions or modules included in the device provided by the embodiments of the present disclosure can be used to execute the method described in the above method embodiments. The specific implementation can refer to the description of the above method embodiments, and for the sake of brevity, it will not be repeated here.

[0153] The embodiment of the present disclosure also provides a computer-readable storage medium on which computer program instructions are stored, and the computer program instructions implement the above method when executed by a processor. The computer-readable storage medium can be a volatile or non-volatile computer-readable storage medium.

[0154] An embodiment of the present disclosure further proposes an electronic device, comprising: a processor; and a memory for storing instructions executable by the processor; wherein the processor is configured to call the instructions stored in the memory to execute the above method.

[0155] The embodiments of the present disclosure also provide a computer program product, including a computer-readable code, or a non-volatile computer-readable storage medium carrying the computer-readable code. When the computer-readable code runs in a processor of an electronic device, the processor in the electronic device executes the above method.

[0156] The electronic device may be provided as a terminal, a server, or a device in other forms.

[0157] Figure 6 A block diagram of an electronic device according to an embodiment of the present disclosure is shown. Figure 6 , the electronic device 1900 can be provided as a server or a terminal device. Figure 6 , the electronic device 1900 includes a processing component 1922, which further includes one or more processors, and a memory resource represented by a memory 1932 for storing instructions executable by the processing component 1922, such as an application. The application stored in the memory 1932 may include one or more modules, each corresponding to a set of instructions. In addition, the processing component 1922 is configured to execute instructions to perform the above method.

[0158] The electronic device 1900 may also include a power supply component 1926 configured to perform power management of the electronic device 1900, a wired or wireless network interface 1950 configured to connect the electronic device 1900 to a network, and an input / output interface 1958. The electronic device 1900 may operate based on an operating system stored in the memory 1932, such as a Microsoft Server operating system (Windows Server 2003). TM ), a graphical user interface operating system launched by Apple (Mac OS X TM ), a multi-user, multi-process computer operating system (Unix TM ), a free and open source Unix-like operating system (Linux TM ), an open source Unix-like operating system (FreeBSD TM ) or similar.

[0159] In an exemplary embodiment, a non-volatile computer-readable storage medium is also provided, such as a memory 1932 including computer program instructions, which can be executed by the processing component 1922 of the electronic device 1900 to perform the above method.

[0160] The present disclosure may be a system, a method and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present disclosure.

[0161] Computer readable storage medium can be a tangible device that can hold and store instructions used by an instruction execution device. Computer readable storage medium can be, for example, (but not limited to) an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination thereof. More specific examples (non-exhaustive list) of computer readable storage medium include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, for example, a punch card or a convex structure in a groove on which instructions are stored, and any suitable combination thereof. The computer readable storage medium used here is not interpreted as a transient signal itself, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagated by a waveguide or other transmission medium (for example, a light pulse by an optical fiber cable), or an electrical signal transmitted by a wire.

[0162] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing / processing device.

[0163] The computer program instructions for performing the operation of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages, such as Smalltalk, C++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. Computer-readable program instructions may be executed completely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or completely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), may be customized by utilizing the state information of the computer-readable program instructions, and the electronic circuit may execute the computer-readable program instructions, thereby realizing various aspects of the present disclosure.

[0164] Various aspects of the present disclosure are described herein with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present disclosure. It should be understood that each box in the flowchart and / or block diagram and the combination of each box in the flowchart and / or block diagram can be implemented by computer-readable program instructions.

[0165] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, thereby producing a machine, so that when these instructions are executed by the processor of the computer or other programmable data processing device, a device that implements the functions / actions specified in one or more boxes in the flowchart and / or block diagram is generated. These computer-readable program instructions can also be stored in a computer-readable storage medium, and these instructions cause the computer, programmable data processing device, and / or other equipment to work in a specific manner, so that the computer-readable medium storing the instructions includes a manufactured product, which includes instructions for implementing various aspects of the functions / actions specified in one or more boxes in the flowchart and / or block diagram.

[0166] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device so that a series of operating steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to implement the functions / actions specified in one or more boxes in the flowchart and / or block diagram.

[0167] The flow chart and block diagram in the accompanying drawings show the possible architecture, function and operation of the system, method and computer program product according to multiple embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a part of a module, program segment or instruction, and a part of the module, program segment or instruction includes one or more executable instructions for realizing the specified logical function. In some alternative implementations, the function marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two continuous square boxes can actually be executed substantially in parallel, and they can sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs the specified function or action, or can be implemented with a combination of special hardware and computer instructions.

[0168] The computer program product may be implemented in hardware, software or a combination thereof. In one optional embodiment, the computer program product is embodied as a computer storage medium, and in another optional embodiment, the computer program product is embodied as a software product, such as a software development kit (SDK) and the like.

[0169] The above description of various embodiments tends to emphasize the differences between the various embodiments. The same or similar aspects can be referenced to each other, and for the sake of brevity, they will not be repeated herein.

[0170] Those skilled in the art will appreciate that, in the above method of specific implementation, the order in which the steps are written does not imply a strict execution order and does not constitute any limitation on the implementation process. The specific execution order of the steps should be determined by their functions and possible internal logic.

[0171] If the technical solution of this application involves personal information, the product using the technical solution of this application has clearly informed the personal information processing rules and obtained the individual's voluntary consent before processing the personal information. If the technical solution of this application involves sensitive personal information, the product using the technical solution of this application has obtained the individual's separate consent before processing the sensitive personal information, and at the same time meets the "explicit consent" requirement. For example, on personal information collection devices such as cameras, clear and prominent signs are set to inform that the personal information collection scope has been entered and personal information will be collected. If the individual voluntarily enters the collection scope, it is deemed that he or she agrees to the collection of his or her personal information; or on the device that processes personal information, the personal information processing rules are notified by obvious signs / information, and the individual's authorization is obtained through pop-up information or by asking the individual to upload his or her personal information; among them, the personal information processing rules may include information such as the personal information processor, the purpose of personal information processing, the processing method, and the type of personal information processed.

[0172] The embodiments of the present disclosure have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and changes will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The selection of terms used herein is intended to best explain the principles of the embodiments, practical applications, or improvements to the technology in the market, or to enable other persons of ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. A PBR material map generation method, characterized in that: include: Collecting a plurality of original images of the target object under different lighting conditions at a target viewing angle, wherein the plurality of original images have a target resolution; According to the multiple original images, a PBR material map prediction model is used to perform multi-stage map prediction to determine a target PBR material map of the target object at the target perspective, wherein the target PBR material map has the target resolution.

2. The method according to claim 1, characterized in that The method of performing multi-stage texture prediction using a PBR material texture prediction model according to the multiple original images to determine a target PBR material texture of the target object at the target perspective includes: For the j-th stage texture prediction, determining a plurality of to-be-processed images corresponding to the j-th stage texture prediction according to the plurality of original images, wherein j is a positive integer greater than or equal to 1; Using the PBR material map prediction model, a plurality of to-be-processed images corresponding to the j-stage map prediction are predicted pixel by pixel to obtain the j-stage PBR material map; When the resolution of the j-th stage PBR material map reaches the target resolution, the j-th stage PBR material map is determined as the target PBR material map.

3. The method according to claim 2, characterized in that The resolution of the multiple images to be processed corresponding to the j-th stage texture prediction is greater than the resolution of the multiple images to be processed corresponding to the j-1-th stage texture prediction, and is less than or equal to the target resolution; The resolution of the j-th stage PBR material map is equal to the resolution of the multiple images to be processed corresponding to the j-th stage map prediction.

4. The method according to claim 1, characterized in that: The PBR material mapping prediction model includes: a first prediction sub-model and a second prediction sub-model; The method of performing multi-stage texture prediction using a PBR material texture prediction model according to the multiple original images to determine a target PBR material texture of the target object at the target perspective includes: According to the plurality of original images, using the first prediction sub-model to perform first stage texture prediction to obtain a first stage PBR material texture; According to the multiple original images and the (i-1)th stage PBR material map, use the second prediction sub-model to perform the i-th stage map prediction to obtain the i-th stage PBR material map, where i is a positive integer greater than 1; When the resolution of the i-th stage PBR texture map reaches the target resolution, the i-th stage PBR texture map is determined as the target PBR texture map.

5. The method according to claim 4, characterized in that The method of performing a first-stage texture prediction using the first prediction sub-model according to the plurality of original images to obtain a first-stage PBR material texture includes: Performing downsampling processing on the multiple original images to obtain multiple to-be-processed images corresponding to the first-stage texture prediction, wherein the resolution of the multiple to-be-processed images corresponding to the first-stage texture prediction is smaller than the target resolution; The multiple images to be processed corresponding to the first stage texture prediction are input into the first prediction sub-model to perform the first stage texture prediction to obtain the first stage PBR material map.

6. The method according to claim 4, characterized in that The method of performing a map prediction of the i-th stage using the second prediction sub-model according to the plurality of original images and the i-1-th stage PBR material map to obtain the i-th stage PBR material map includes: Performing downsampling processing on the multiple original images to obtain multiple to-be-processed images corresponding to the i-th stage texture prediction, wherein the resolution of the multiple to-be-processed images corresponding to the i-th stage texture prediction is greater than the resolution of the multiple to-be-processed images corresponding to the i-1-th stage texture prediction, and is less than or equal to the target resolution; Performing upsampling processing on the i-1th stage PBR material map to obtain a PBR material map to be processed corresponding to the i-th stage map prediction, wherein the PBR material map to be processed corresponding to the i-th stage map prediction and the multiple images to be processed corresponding to the i-th stage map prediction have the same resolution; The multiple images to be processed corresponding to the i-th stage texture prediction and the PBR material map to be processed corresponding to the i-th stage texture prediction are input into the second prediction sub-model to perform the i-th stage texture prediction to obtain the i-th stage PBR material map.

7. The method according to claim 4, characterized in that The method of performing a map prediction of the i-th stage using the second prediction sub-model according to the plurality of original images and the i-1-th stage PBR material map to obtain the i-th stage PBR material map includes: Fusing the multiple original images and the (i-1)th stage PBR material map to obtain a fused image; Sampling the fused image to obtain an image to be processed corresponding to the i-th stage texture prediction, wherein the resolution of the image to be processed corresponding to the i-th stage texture prediction is greater than the resolution of the image to be processed corresponding to the i-1-th stage texture prediction, and is less than or equal to the target resolution; The image to be processed corresponding to the i-th stage texture prediction is input into the second prediction sub-model to perform the i-th stage texture prediction to obtain the i-th stage PBR material map.

8. The method according to claim 1, characterized in that The method of performing multi-stage texture prediction using a PBR material texture prediction model according to the multiple original images to determine a target PBR material texture of the target object at the target perspective includes: When the target resolution is higher than the resolution threshold corresponding to the PBR material map prediction model, each original image is split into image blocks to obtain a plurality of image block groups, wherein the plurality of image blocks included in each image block group correspond to the same position area in the plurality of original images, and the resolution of the plurality of image blocks included in each image block group is less than or equal to the resolution threshold; According to the multiple image blocks included in each image block group, the PBR material map prediction model is used to perform multi-stage map prediction to determine the PBR material map block corresponding to each image block group; The PBR material map blocks corresponding to the multiple image block groups are merged to obtain the target PBR material map.

9. A three-dimensional object material scanning method, characterized in that: include: Acquire multiple original images of the target object captured at multiple capture angles and corresponding to different lighting conditions, wherein the multiple capture angles can surround and cover the target object, and the multiple original images captured at each capture angle have a target resolution; According to the multiple original images collected at each acquisition perspective, a PBR material map prediction model is used to perform multi-stage map prediction to determine the target PBR material map of the target object at each acquisition perspective, wherein the target PBR material map at each acquisition perspective has the target resolution; Reconstructing the target object in three dimensions according to the multiple original images collected at each collecting viewing angle to obtain an initial three-dimensional model corresponding to the target object; According to the target PBR material map of the target object at each acquisition viewing angle, the initial three-dimensional model is material mapped to obtain a target three-dimensional model corresponding to the target object.

10. A three-dimensional object material scanning device, characterized in that: include: A carrying part, an image acquisition part and a plurality of light sources; The image acquisition part is arranged at the center of the carrying part, and the plurality of light sources are arranged on the carrying part around the image acquisition part; The image acquisition unit can acquire images of the target object illuminated by the multiple light sources at multiple acquisition viewing angles, and the multiple light sources can illuminate the target object under multiple lighting conditions; Among them, for any acquisition viewing angle, the image acquisition unit acquires multiple original images of the target object under different lighting conditions, and the multiple original images acquired at each acquisition viewing angle are used to determine the target PBR material map of the target object at the acquisition viewing angle.

11. The device according to claim 10, characterized in that The device further includes a control unit, the control unit being used to control at least one of turning on and off the plurality of light sources and the illumination conditions, so that the plurality of light sources can illuminate the target object under a variety of illumination conditions; The control unit is further used to control the acquisition viewing angle of the image acquisition unit, and control the image acquisition unit to acquire at least one original image for the target object under each lighting condition at any acquisition viewing angle.

12. The device according to claim 10 or 11, characterized in that The device further comprises a computing unit, which is configured to execute the method according to any one of claims 1 to 9.

13. An electronic device, characterized in that: include: processor; a memory for storing processor-executable instructions; The processor is configured to call the instructions stored in the memory to execute the method described in any one of claims 1 to 9.

14. A computer-readable storage medium having computer program instructions stored thereon, characterized in that: When the computer program instructions are executed by a processor, the method according to any one of claims 1 to 9 is implemented.

15. A computer program product, characterized in that A non-volatile computer-readable storage medium including computer-readable code or carrying computer-readable code; When the computer readable code runs in a processor of an electronic device, the processor in the electronic device executes the method according to any one of claims 1 to 9.

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