Method for optimizing path tracing precomputation based on material parameter technology

Abstract

The application discloses a method for optimizing path tracking precalculation based on material parameter technology. The application realizes the dual improvement of rendering efficiency and image quality, and meanwhile, provides higher system flexibility and user friendliness. These technical effects make the method have important application value and competitive advantage in the offline rendering industry. The application not only improves the rendering efficiency and image quality, but also provides an efficient, simple and reliable offline rendering solution for users through intelligent management and optimization.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of rendering technology, and particularly relates to a method for optimizing path tracing precomputation based on material parameter technology. BACKGROUND

[0002] In the field of three-dimensional computer graphics, path tracing technology is an advanced rendering method that can simulate the propagation process of light in a scene, including reflection, refraction and scattering phenomena, thereby generating highly realistic images. However, due to the need for path tracing to calculate a large number of light paths, the computational cost is very high, especially when dealing with scenes with complex materials and fine details.

[0003] Precomputation is an important step in path tracing, which usually involves pre-computing lighting information in the scene to quickly obtain this information during actual rendering. Traditional precomputation methods often do not take into account the differences in the effects of different materials on light propagation, resulting in unnecessary computational burden when dealing with scenes with multiple material types and properties, reducing rendering efficiency.

[0004] With the development of rendering technology and the increasing demand for rendering quality from users, how to improve rendering efficiency while ensuring image quality has become an important challenge facing the industry.

[0005] Therefore, the prior art has defects and needs to be improved. SUMMARY

[0006] The technical problem to be solved by the present application is to provide a method for optimizing path tracing precomputation based on material parameter technology to realize a more efficient and intelligent rendering process.

[0007] The technical solution of the present application is as follows: a method for optimizing path tracing precomputation based on material parameter technology is provided, comprising the following steps:

[0008] S1: Scene upload stage; the user logs in to the management system of the offline rendering platform through security authentication; the user uploads or specifies the scene file to be rendered, which contains the geometric data, material information and light source configuration of the scene;

[0009] S2: Scene analysis stage; the management system of the offline rendering platform automatically parses the scene file and extracts the material properties of each object; and uses a clustering algorithm to group materials, grouping materials with similar properties together. The material properties include color, transparency, roughness, metallicness, refractive index and reflectivity. The clustering algorithm uses any one of K-means clustering, mean shift clustering, density-based clustering method, expectation maximization (EM) clustering with Gaussian mixture model (GMM), agglomerative hierarchical clustering and graph community detection.

[0010] S3: Pre-computation strategy customization stage; for each material type, the management system of the offline rendering platform determines the most suitable pre-computation method according to its optical properties; and formulates the depth and sampling rate of ray tracing, as well as other related parameters of light and material interaction used in the path tracing process. Determining the most suitable pre-computation method according to its optical properties is: for high reflectivity metal materials, more specular reflection calculation is adopted; for transparent or translucent materials, more refraction and subsurface scattering calculation is adopted; wherein more refers to more than 50%. Further, the depth and sampling rate of ray tracing adopts adaptive sampling technology, which adjusts the sampling density and depth of ray tracing according to the characteristics of the material, so as to reduce unnecessary calculation and improve rendering quality.

[0011] S4: Pre-computation execution stage; the management system of the offline rendering platform executes the pre-computation operation according to the pre-computation strategy to generate illumination information, including the contribution values of direct and indirect illumination; the pre-computation result will be stored in the cache for quick access and use in the rendering process.

[0012] S5: Rendering execution stage; according to the optimized pre-computation parameters, the management system of the offline rendering platform executes the final rendering process using the offline rendering platform to generate an image with optimized lighting, i.e. the rendering result; the steps of the rendering process include ray tracing, shadow calculation, global illumination synthesis, etc. In step S5, if the light source in the scene changes during real-time rendering, the management system of the offline rendering platform automatically reclassifies the material and adjusts the pre-computation strategy according to the change; that is, it reenters step S2.

[0013] S6: Output and feedback stage; users can view the rendering result through thumbnails and download or further post-processing (such as manually changing rendering parameters to re-render if not satisfied with the rendering result) through the management system of the offline rendering platform.

[0014] Further, the method of optimizing path tracing pre-computation based on material parameters further comprises S7: resource recycling and maintenance stage: after completing the rendering task, the management system of the offline rendering platform recycles the resources allocated to the task and performs necessary maintenance work such as cache cleaning and hardware checking to prepare for the next rendering task.

[0015] Further, the management system of the offline rendering platform evaluates the performance of the pre-computation and rendering process through performance monitoring tools, including rendering time, resource usage and possible optimization suggestions.

[0016] The present application can effectively improve the rendering efficiency: by intelligently analyzing and applying material parameters to reduce unnecessary calculations in the path tracing pre-computation process, thereby accelerating the overall rendering time.

[0017] The present application can effectively enhance image quality: while ensuring rendering speed, the pre-computation method can consider the influence of different materials on light propagation to maintain or improve the quality of the final image.

[0018] The present application can effectively optimize resource allocation: according to the characteristics of the material, the calculation resources are reasonably allocated, avoiding excessive calculation on materials with less visual impact, while ensuring that key materials are adequately processed.

[0019] The present application can effectively improve the level of automation: reduce user manual adjustment and intervention in the pre-computation stage, make the rendering process more automated, reduce the difficulty of operation and technical threshold.

[0020] The present application can effectively adapt to complex scenes: for complex scenes containing multiple types and properties of materials, an effective processing method is provided to solve the problems that traditional pre-computation methods cannot solve.

[0021] The present application can effectively expand the application range of technology: through optimizing the pre-computation method, the path tracing technology can be more widely applied to movies, games, building visualization and other industries that require high-quality rendering.

[0022] The present application can effectively promote sustainable development: by reducing the consumption of computing resources and energy use, it helps to promote the sustainable development of the rendering industry.

[0023] Overall, the present application aims to provide an efficient, intelligent and environmentally friendly solution for offline rendering platforms by introducing optimization techniques based on material parameters, to meet the growing demand for rendering and quality standards.

[0024] By adopting the above scheme, the present application provides a method for optimizing path tracing pre-computation based on material parameter technology, which has the following technical effects:

[0025] Performance improvement: by reducing the number of light sources that must be processed, the computational complexity of shadow pre-computation is reduced, significantly improving rendering speed and overall system performance.

[0026] Resource conservation: the optimized method more efficiently utilizes computing resources, reducing dependence on high-performance hardware, and reducing hardware costs and energy consumption.

[0027] Image quality improvement: despite the reduction in computational load, the final output shadow effect still maintains high quality and realism due to accurate processing of key light sources and intelligent synthesis technology.

[0028] Adaptive Enhancement: This method can adapt to different rendering environments and scene changes, especially for dynamic changing light sources and complex scenes, providing flexible processing strategies.

[0029] User Experience Optimization: Accelerates rendering time, providing faster feedback for users, while simplifying the operation process, as automated clustering and pre-computation reduce the need for manual adjustments.

[0030] Scalability: This technology can be easily integrated into existing rendering systems, whether in small-scale personal studios or large-scale rendering farms.

[0031] Improved Intelligence Level: Intelligent decision support is introduced, such as automatic identification of key light sources and automatic adjustment of pre-computation parameters, improving the intelligence level of the rendering system.

[0032] Compatibility and Maintainability: With standardized clustering algorithms and pre-computation techniques, this method is easy to maintain and upgrade, and is also compatible with multiple file formats and rendering engines.

[0033] In summary, the shadow pre-computation optimization method based on light source clustering technology has achieved dual improvement in rendering efficiency and image quality, while providing higher system flexibility and user friendliness. These technical effects make this method have important application value and competitive advantage in the offline rendering industry. BRIEF DESCRIPTION OF DRAWINGS

[0034] Figure 1 The flowchart of the method of the present application. DETAILED DESCRIPTION

[0035] The present application will be described in detail below in conjunction with the drawings and specific examples.

[0036] Please refer to Figure 1 , the present application provides a method for optimizing path tracing pre-computation based on material parameter technology, including the following steps:

[0037] S1: Scene upload stage; the user logs in to the management system of the offline rendering platform through security authentication; the user uploads or specifies the scene file to be rendered, which contains the geometric data, material information and light source configuration of the scene;

[0038] S2: Scene analysis stage; the management system of the offline rendering platform automatically parses the scene file and extracts the material properties of each object; and uses clustering algorithms to group materials with similar properties. The material properties include: color, transparency, roughness, metallic degree, refractive index, reflectivity. The clustering algorithm uses any one of: K-means clustering, mean shift clustering, density-based clustering method, expectation maximization (EM) clustering based on Gaussian mixture model (GMM), agglomerative hierarchical clustering, and graph community detection.

[0039] S3: Pre-computation strategy customization stage; for each material type, the management system of the offline rendering platform determines the most suitable pre-computation method according to its optical properties; and determines the depth and sampling rate of ray tracing, as well as other related parameters of light and material interaction used in the path tracing process. According to its optical properties to determine the most suitable pre-computation method: for high reflectivity metal materials, more specular reflection calculation is used; for transparent or translucent materials, more refraction and subsurface scattering calculation is used; where more refers to a proportion greater than 50%. Further, the depth and sampling rate of ray tracing use adaptive sampling technology, which adjusts the sampling density and depth of ray tracing according to the characteristics of the material to reduce unnecessary calculations and improve rendering quality.

[0040] S4: Pre-computation execution stage; the management system of the offline rendering platform executes pre-computation operations according to the pre-computation strategy to generate illumination information, including direct and indirect lighting contribution values; the pre-computation results will be stored in the cache for quick access and use during the rendering process.

[0041] S5: Rendering execution stage; according to the optimized pre-computation parameters, the management system of the offline rendering platform uses the offline rendering platform to execute the final rendering process to generate images with optimized lighting, i.e. the rendering result; the steps of the rendering process include ray tracing, shadow calculation, global illumination synthesis, etc. In step S5, during real-time rendering, if the light source in the scene changes, the management system of the offline rendering platform automatically reclassifies materials and adjusts the pre-computation strategy according to the changes; i.e. re-entering step S2.

[0042] S6: Output and feedback stage; users can view the rendering results through thumbnails and download or further post-processing (such as manually changing rendering parameters to re-render if not satisfied with the rendering results) through the management system of the offline rendering platform.

[0043] In this embodiment, the method of optimizing path tracing precomputation based on material parameters also includes, S7: resource recycling and maintenance phase: after completing the rendering task, the management system of the offline rendering platform recycles the resources allocated to the task and performs necessary maintenance work, such as cache cleaning and hardware inspection, to prepare for the next rendering task.

[0044] In this embodiment, the management system of the offline rendering platform evaluates the performance of the precomputation and rendering process through performance monitoring tools, including rendering time, resource usage, and possible optimization suggestions.

[0045] Specifically, the present application operates as follows.

[0046] Material parameter analysis: First, analyze the material properties of objects in the scene, extract the parameters of each material, including but not limited to diffuse reflection, specular reflection, transparency, refractive index, etc.

[0047] Material clustering processing: Use clustering algorithms to classify materials into different categories based on material parameters for subsequent targeted processing. Preferably, use the K-means clustering algorithm.

[0048] Precomputation strategy customization: Develop appropriate precomputation strategies based on the characteristics of different categories of materials. For example, for high-gloss materials, more specular reflection calculations may be needed; for semi-transparent or transparent materials, focus on refraction and subsurface scattering effects.

[0049] Adaptive sampling optimization: In the path tracing process, use adaptive sampling techniques to adjust the sampling density and depth of ray tracing based on the characteristics of the material to reduce unnecessary calculations and improve rendering quality.

[0050] Precomputation result caching: Effectively store and manage the lighting information obtained through precomputation to facilitate fast retrieval and application during the rendering process, reducing redundant calculations.

[0051] Real-time dynamic adjustment: During the rendering process, dynamically adjust the precomputation strategy and related parameters based on real-time monitoring of rendering performance and user-specified quality requirements to adapt to changing rendering needs.

[0052] Synthesis and post-processing: Finally, synthesize the contribution values obtained through precomputation of different materials, which may involve color correction, denoising, blurring, and other post-processing steps to ensure the visual effect of the image.

[0053] Performance evaluation and feedback: Implement a performance evaluation mechanism to evaluate the optimized precomputation method in terms of rendering time, resource usage efficiency, and image quality, and continuously improve based on feedback.

[0054] Through the above technical solutions, the application can perform targeted pre-computation optimization according to different material characteristics, significantly improve the rendering efficiency of path tracing, while maintaining or improving the quality of the rendered image, and is suitable for offline rendering platforms and high-performance computing environments.

[0055] In summary, the application provides a method for optimizing path tracing pre-computation based on material parameter technology, which has the following technical effects:

[0056] Performance improvement: By reducing the number of light sources that must be processed, the computational complexity of shadow pre-computation is reduced, thereby significantly improving rendering speed and overall system performance.

[0057] Resource conservation: The optimized method more efficiently utilizes computing resources, reduces dependence on high-performance hardware, and reduces hardware costs and energy consumption.

[0058] Image quality improvement: Despite the reduction in computational load, the final output shadow effect still maintains high quality and realism due to accurate processing of key light sources and intelligent synthesis technology.

[0059] Adaptability enhancement: The method can adapt to different rendering environments and scene changes, especially for dynamic changing light sources and complex scenes, providing flexible processing strategies.

[0060] User experience optimization: Accelerating rendering time provides faster feedback to users, while simplifying the operation process as automated clustering and pre-computation reduces the need for user manual adjustments.

[0061] Scalability: The technology can be easily integrated into existing rendering systems, whether in small-scale personal studios or large-scale rendering farms.

[0062] Intelligent level improvement: The introduction of intelligent decision support, such as automatic identification of key light sources and automatic adjustment of pre-computation parameters, improves the intelligent level of the rendering system.

[0063] Compatibility and maintainability: As the method uses standardized clustering algorithms and pre-computation techniques, it is easy to maintain and upgrade, and is also compatible with multiple file formats and rendering engines.

[0064] In summary, the shadow pre-computation optimization method based on light source clustering technology achieves dual improvement in rendering efficiency and image quality, while providing higher system flexibility and user friendliness. These technical effects make the method have important application value and competitive advantage in the offline rendering industry.

[0065] The above merely preferred embodiments of the present application are not intended to limit the present application, and any modification, equivalent replacement, and improvement made within the spirit and principle of the present application shall fall within the scope of protection of the present application.

Claims

1. A method for optimizing path tracing pre-calculation based on material parameter technology, characterized in that, Includes the following steps: S1: Users log in to the management system of the offline rendering platform through security authentication; users upload or specify the scene file to be rendered, which contains the scene's geometric data, material information and light source configuration; S2: The offline rendering platform's management system automatically parses the scene files, extracts the material properties of each object, and uses a clustering algorithm to group the materials together, grouping materials with similar properties together. S3: For each type of material, the offline rendering platform's management system determines the most suitable pre-calculation method based on its optical characteristics; and sets the ray tracing depth and sampling rate, as well as other relevant parameters for the interaction between light and material used in the path tracing process; S4: The management system of the offline rendering platform performs pre-computation operations according to the pre-computation strategy to generate lighting information, including the contribution values ​​of direct lighting and indirect lighting; S5: Based on the optimized pre-calculated parameters, the management system of the offline rendering platform uses the offline rendering platform to execute the final rendering process and generate an image with optimized lighting, i.e., the rendering result. S6: Users can view the rendering results through thumbnails and download or perform further post-processing through the management system of the offline rendering platform; Based on its optical properties, the most suitable pre-calculation method is determined as follows: for highly reflective metallic materials, more specular reflection calculations are used; for transparent or translucent materials, more refraction and subsurface scattering calculations are used; where "more" means a proportion greater than 50%.

2. The method for optimizing path tracing pre-calculation based on material parameter technology according to claim 1, characterized in that, It also includes S7: After completing a rendering task, the offline rendering platform's management system reclaims the resources allocated to that task and performs necessary maintenance to prepare for the next rendering task.

3. The method for optimizing path tracing pre-calculation based on material parameter technology according to claim 1, characterized in that, In step S5, if the light source in the scene changes during real-time rendering, the offline rendering platform's management system automatically readjusts the material classification and pre-calculation strategy based on the change; that is, it re-enters step S2.

4. The method for optimizing path tracing pre-calculation based on material parameter technology according to claim 1, characterized in that, The material properties include: color, transparency, roughness, metallicity, refractive index, and reflectivity.

5. The method for optimizing path tracing pre-calculation based on material parameter technology according to claim 1, characterized in that, The rendering process includes ray tracing, shadow calculation, and global illumination compositing.

6. The method for optimizing path tracing pre-calculation based on material parameter technology according to claim 1, characterized in that, Ray tracing depth and sampling rate employ adaptive sampling technology, adjusting the ray tracing sampling density and depth according to the characteristics of the material to reduce useless calculations and improve rendering quality.

7. The method for optimizing path tracing pre-calculation based on material parameter technology according to claim 1, characterized in that, The pre-calculated results will be stored in the cache for quick access and use during the rendering process.

8. The method for optimizing path tracing pre-calculation based on material parameter technology according to claim 1, characterized in that, The management system of the offline rendering platform uses performance monitoring tools to evaluate the performance of the pre-computation and rendering processes, including rendering time, resource usage, and possible optimization suggestions.

9. The method for optimizing path tracing pre-calculation based on material parameter technology according to claim 1, characterized in that, The clustering algorithm used is any one of the following: K-means clustering, mean-shift clustering, density-based clustering, expectation-maximum clustering with Gaussian mixture model, agglomerative hierarchical clustering, and graph community detection.

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