Method for optimizing shadow pre-calculation based on light source clustering technology

Abstract

The invention discloses a method for optimizing shadow pre-calculation based on a light source clustering technology, which comprises the following steps of: firstly, uploading a scene file, then carrying out light source attribute analysis, then setting rendering parameters, submitting the scene file and the rendering parameters to a cloud rendering system, and then clustering a light source through script processing of a node machine of a cloud rendering farm, so as to obtain the shadow pre-calculation. Key light source identification is carried out; performing machine resource allocation, pre-calculation allocation and calculation power allocation to generate a chartlet, performing shadow synthesis, performing dynamic adjustment, performing rendering execution, generating a rendering output graph, and outputting the rendering output graph. According to the method, the rendering efficiency and the shadow quality are improved, and an efficient, simple, convenient and reliable off-line rendering solution is provided for a user through intelligent management and optimization.

Description

Technical Field

[0001] The present invention relates to the field of rendering technology, and in particular to a method for optimizing shadow pre-calculation based on light source clustering technology. Background Art

[0002] In 3D graphics rendering, shadow generation is a computationally intensive task, especially in scenes with a large number of light sources. Precomputing shadow maps is a common technique to improve rendering performance, but the cost of precomputation increases as the number of light sources in a scene increases. Therefore, an effective method is needed to optimize the shadow precomputation process under a large number of light sources.

[0003] Therefore, the prior art has defects and needs to be improved. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a method for optimizing shadow pre-calculation based on light source clustering technology, solve the efficiency, quality and resource utilization problems existing in the offline rendering industry, and provide users with faster and higher quality rendering services.

[0005] The technical solution of the present invention is as follows: a method for optimizing shadow pre-calculation based on light source clustering technology is provided, comprising the following steps.

[0006] S1: The user uploads or specifies the scene to be rendered on the user side, which includes the scene's geometric data, material information, and light source configuration.

[0007] S2: The user side parses the light source data in the scene file and extracts the position, direction, color, intensity, and influence range attributes of each light source.

[0008] S3: The user sets rendering parameters on the client side and submits the scene file and rendering configuration to the cloud rendering system.

[0009] S4: Cluster the light sources through script processing of the node machines of the cloud rendering system. The specific method of light source clustering is: use a clustering algorithm (such as K-means, DBSCAN, etc.) to group the light sources and cluster light sources with similar properties together.

[0010] S5: performing key light source identification; that is, for each cluster, the system identifies the key light source according to the degree of influence of the light source on the scene (for example, based on factors such as light intensity and angle).

[0011] S6: Perform machine resource allocation and pre-calculation allocation; that is, for key light sources, the system allocates more computing resources for standard shadow pre-calculation; for non-key light sources, low resolution or approximate methods are used for pre-calculation.

[0012] S7: Generate a map after computing power is allocated; that is, the cloud rendering system generates a shadow map based on the pre-calculated results, ensuring high quality and high resolution for the shadows produced by key light sources; for other light sources, a lower resolution may be used.

[0013] S8: Perform shadow synthesis; the shadow maps of all light sources are synthesized into the final shadow effect.

[0014] S9: Rendering execution; that is, according to the optimized pre-calculated parameters, the cloud rendering system performs the final rendering process to generate an image or animation with optimized shadows.

[0015] S10: The cloud rendering system generates a rendering output image and outputs it to the user end.

[0016] In step S10, the cloud rendering system also outputs a rendering report, which includes: rendering time, resource usage and possible optimization suggestions. The optimization suggestions are feedback for this rendering to facilitate the user to further adjust rendering parameters and other data.

[0017] In step S10, it also includes: the user views the rendering result through the user terminal, and the user downloads or performs further post-processing through the user terminal.

[0018] Step S9 also includes: during the real-time rendering process, if the light source in the scene changes, the cloud rendering system automatically re-adjusts the light source clustering and pre-calculation strategy, that is, enters step S4.

[0019] In step S8, the shadows are synthesized into a blending, blurring, and sharpening process of the shadow map to ensure visual consistency and realism.

[0020] The user end is a web page end or a client end.

[0021] The present invention can improve rendering efficiency by reducing the number of light sources that must be processed and lowering the computational cost of shadow pre-calculation, thereby speeding up the overall rendering speed.

[0022] The present invention can enhance image quality by avoiding excessive calculation and performance degradation caused by a large number of light sources while maintaining high-quality shadow effects.

[0023] The present invention can optimize resource usage: reasonably allocate computing resources to ensure the best rendering effect under limited hardware resources.

[0024] The present invention can improve user experience: by reducing rendering time, users can obtain rendering results more quickly while maintaining high visual quality output.

[0025] The present invention can adapt to complex scenes: for complex scenes with a large number of dynamic or static light sources, an effective processing method is provided to cope with challenges that cannot be solved by traditional single pre-calculation methods.

[0026] The present invention can flexibly respond to changes: during real-time rendering, the pre-calculation strategy is dynamically adjusted according to the scene to adapt to changes in light source and environment.

[0027] The present invention can expand the scope of technical application: by providing a scalable shadow pre-computation optimization method, it is suitable for offline rendering platforms of various sizes and different application scenarios.

[0028] The present invention can reduce the difficulty of operation: automate clustering and pre-calculation processes, reduce the need for manual adjustment by users, and reduce the difficulty of operation and technical threshold.

[0029] By adopting the above scheme, the present invention provides a method for optimizing shadow pre-calculation based on light source clustering technology, which has the following technical effects:

[0030] Performance improvements: The computational complexity of shadow precomputation has been reduced by reducing the number of lights that must be processed, resulting in significantly faster rendering and overall system performance.

[0031] Resource conservation: The optimized method utilizes computing resources more efficiently, reduces dependence on high-performance hardware, and reduces hardware costs and energy consumption.

[0032] Improved image quality: Despite the reduced computational effort, the final output shadow effects remain high quality and realistic thanks to accurate handling of key light sources and smart compositing techniques.

[0033] Enhanced adaptability: This method can adapt to different rendering environments and scene changes, especially for dynamically changing light sources and complex scenes, and provides a flexible processing strategy.

[0034] User experience optimization: Speeds up rendering time, provides users with faster feedback, and simplifies the operation process because automated clustering and pre-calculation reduce the need for manual adjustments by users.

[0035] Scalability: The technology can be easily integrated into existing rendering systems, whether in a small personal studio or a large-scale render farm.

[0036] Improved intelligence level: The introduction of intelligent decision support, such as automatic identification of key light sources and automatic adjustment of pre-calculation parameters, improves the intelligence level of the rendering system.

[0037] Compatibility and maintainability: Due to the use of standardized clustering algorithms and pre-computation techniques, this method is easy to maintain and upgrade, while also being compatible with a variety of file formats and rendering engines.

[0038] In general, the shadow pre-computation optimization method based on light source clustering technology has technically achieved a double 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 advantages in the offline rendering industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0040] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0041] See also Figure 1 The present invention provides a method for optimizing shadow pre-calculation based on light source clustering technology, which includes the following steps.

[0042] S1: The user uploads or specifies the scene to be rendered on the user side, which includes the scene's geometric data, material information, and light source configuration.

[0043] S2: The user side parses the light source data in the scene file and extracts the position, direction, color, intensity, and influence range attributes of each light source.

[0044] S3: The user sets rendering parameters on the user side and submits the scene file and rendering configuration to the cloud rendering system (cloud rendering farm).

[0045] S4: Cluster the light sources through script processing of the node machines of the cloud rendering system. The specific method of light source clustering is: use a clustering algorithm (such as K-means, DBSCAN, etc.) to group the light sources and cluster light sources with similar properties together.

[0046] S5: performing key light source identification; that is, for each cluster, the system identifies the key light source according to the degree of influence of the light source on the scene (for example, based on factors such as light intensity and angle).

[0047] S6: Perform machine resource allocation and pre-calculation allocation; that is, for key light sources, the system allocates more computing resources for standard shadow pre-calculation; for non-key light sources, low resolution or approximate methods are used for pre-calculation.

[0048] S7: Generate a map after computing power is allocated; that is, the cloud rendering system generates a shadow map based on the pre-calculated results, ensuring high quality and high resolution for the shadows produced by key light sources; for other light sources, a lower resolution may be used.

[0049] S8: Perform shadow synthesis; the shadow maps of all light sources are synthesized into the final shadow effect.

[0050] S9: Rendering execution; that is, according to the optimized pre-calculated parameters, the cloud rendering system performs the final rendering process to generate an image or animation with optimized shadows.

[0051] S10: The cloud rendering system generates a rendering output image and outputs it to the user end.

[0052] In step S10, the cloud rendering system also outputs a rendering report, which includes: rendering time, resource usage, and possible optimization suggestions.

[0053] In step S10, it also includes: the user views the rendering result through the user terminal, and the user downloads or performs further post-processing through the user terminal.

[0054] Step S9 also includes: during the real-time rendering process, if the light source in the scene changes, the cloud rendering system automatically re-adjusts the light source clustering and pre-calculation strategy, that is, enters step S4.

[0055] In step S8, the shadows are synthesized into a blending, blurring, and sharpening process of the shadow map to ensure visual consistency and realism.

[0056] The user end is a web page end or a client end.

[0057] The present invention first analyzes the properties of all light sources in the scene, including but not limited to position, direction, color, intensity and impact range. These properties will be used in the subsequent light source clustering process.

[0058] The present invention utilizes clustering algorithms (such as K-means, hierarchical clustering, etc.) to group light sources according to their properties. The purpose is to group light sources with similar properties into the same group, thereby reducing the number of unique shadow calculations.

[0059] The present invention identifies the key light sources that contribute most to the scene shadow in each cluster, which will have a decisive impact on the final shadow quality.

[0060] The present invention performs standard pre-calculation on key light sources to generate high-quality shadow maps. For non-key light sources, lower resolution or approximate methods can be used for pre-calculation to reduce the computational burden.

[0061] The present invention combines the pre-calculated shadow maps of all light sources into the final shadow effect. This may require the use of specific blending, blurring or other compositing techniques to ensure visual consistency and realism.

[0062] The present invention dynamically adjusts the light source clustering and pre-calculation strategy according to the requirements of real-time rendering. If the light source in the scene changes, the system can automatically re-cluster and adjust the pre-calculation parameters.

[0063] The present invention implements a performance monitoring system to evaluate the effect of the optimized shadow pre-computation method, including rendering time and shadow quality. Clustering parameters and pre-computation strategies are adjusted according to the monitoring results to achieve the best performance and quality balance.

[0064] The above solution can significantly improve rendering efficiency while ensuring shadow quality, especially when dealing with complex scenes with a large number of light sources. This optimization method based on light source clustering technology is suitable for offline rendering platforms and helps improve the overall performance and user experience of rendering jobs.

[0065] In summary, the present invention provides a method for optimizing shadow pre-calculation based on light source clustering technology, which has the following technical effects:

[0066] Performance improvements: The computational complexity of shadow precomputation has been reduced by reducing the number of lights that must be processed, resulting in significantly faster rendering and overall system performance.

[0067] Resource conservation: The optimized method utilizes computing resources more efficiently, reduces dependence on high-performance hardware, and reduces hardware costs and energy consumption.

[0068] Improved image quality: Despite the reduced computational effort, the final output shadow effects remain high quality and realistic thanks to accurate handling of key light sources and smart compositing techniques.

[0069] Enhanced adaptability: This method can adapt to different rendering environments and scene changes, especially for dynamically changing light sources and complex scenes, and provides a flexible processing strategy.

[0070] User experience optimization: Speeds up rendering time, provides users with faster feedback, and simplifies the operation process because automated clustering and pre-calculation reduce the need for manual adjustments by users.

[0071] Scalability: The technology can be easily integrated into existing rendering systems, whether in a small personal studio or a large-scale render farm.

[0072] Improved intelligence level: The introduction of intelligent decision support, such as automatic identification of key light sources and automatic adjustment of pre-calculation parameters, improves the intelligence level of the rendering system.

[0073] Compatibility and maintainability: Due to the use of standardized clustering algorithms and pre-computation techniques, this method is easy to maintain and upgrade, while also being compatible with a variety of file formats and rendering engines.

[0074] In general, the shadow pre-computation optimization method based on light source clustering technology has technically achieved a double 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 advantages in the offline rendering industry.

[0075] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for optimizing shadow pre-calculation based on light source clustering technology, characterized in that: The following steps are involved: S1: The user uploads or specifies the scene to be rendered on the user side, which includes the scene's geometric data, material information, and light source configuration; S2: The user side parses the light source data in the scene file and extracts the position, direction, color, intensity, and influence range attributes of each light source; S3: The user sets rendering parameters on the user side and submits the scene file and rendering configuration to the cloud rendering system; S4: clustering the light sources through script processing of the node machines of the cloud rendering system; the specific method of light source clustering is: using a clustering algorithm to group the light sources and clustering light sources with similar properties together; S5: performing key light source identification; that is, for each cluster, the system identifies the key light source according to the degree of influence of the light source on the scene; S6: Perform machine resource allocation and pre-calculation allocation; that is, for key light sources, the system allocates more computing resources to perform standard shadow pre-calculation; for non-key light sources, low resolution or approximate methods are used for pre-calculation; S7: Generate a map after computing power is allocated; That is, the cloud rendering system generates shadow maps based on pre-calculated results, ensuring high quality and high resolution for shadows produced by key light sources; for other light sources, lower resolution may be used; S8: Perform shadow synthesis; the shadow maps of all light sources are synthesized into the final shadow effect; S9: Rendering execution; That is, based on the optimized pre-calculated parameters, the cloud rendering system performs the final rendering process to generate an image or animation with optimized shadows; S10: The cloud rendering system generates a rendering output image and outputs it to the user end.

2. The method for optimizing shadow pre-calculation based on light source clustering technology according to claim 1, characterized in that: In step S10, the cloud rendering system also outputs a rendering report, which includes: rendering time, resource usage, and possible optimization suggestions.

3. The method for optimizing shadow pre-calculation based on light source clustering technology according to claim 1, characterized in that: In step S10, it also includes: the user views the rendering result through the user terminal, and the user downloads or performs further post-processing through the user terminal.

4. The method for optimizing shadow pre-calculation based on light source clustering technology according to claim 1, characterized in that: Step S9 also includes: during the real-time rendering process, if the light source in the scene changes, the cloud rendering system automatically re-adjusts the light source clustering and pre-calculation strategy, that is, enters step S4.

5. The method for optimizing shadow pre-calculation based on light source clustering technology according to claim 1, characterized in that: In step S8, the shadows are synthesized into a blending, blurring, and sharpening process of the shadow map to ensure visual consistency and realism.

6. The method for optimizing shadow pre-calculation based on light source clustering technology according to claim 1, characterized in that: The user end is a web page end or a client end.

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