A method for optimizing shadow precomputation based on light source clustering technique

Abstract

The application discloses a method for optimizing shadow precomputation based on light source clustering technology, which comprises the following steps: firstly, uploading a scene file; secondly, analyzing light source properties; thirdly, setting rendering parameters and submitting the scene file and the rendering parameters to a cloud rendering system; fourthly, clustering light sources through script processing of node machines of the cloud rendering farm, and then identifying key light sources; fifthly, allocating machine resources and precomputing allocation; sixthly, generating a map after power allocation; seventhly, synthesizing shadows; eighthly, dynamically adjusting; ninthly, performing rendering; and tenthly, generating and outputting a rendering output image. The application not only improves rendering efficiency and shadow 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 shadow precomputation based on light source clustering technology. BACKGROUND

[0002] In three-dimensional graphics rendering, the generation of shadows 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 when the number of light sources in the scene increases, the cost of precomputation also 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

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

[0005] The technical solution of the present application is as follows: a method for optimizing shadow precomputation 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 end, which contains the geometric data, material information and light source configuration of the scene.

[0007] S2: The user end 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 the rendering parameters on the user end and submits the scene file and rendering configuration to the cloud rendering system.

[0009] S4: The light sources are clustered through the script processing of the node machines of the cloud rendering system. The specific way of light source clustering is to use clustering algorithms (such as K-means, DBSCAN, etc.) to group the light sources, and to gather the light sources with similar attributes together.

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

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

[0012] S7: After the distribution of computing power, generate the map; that is, the cloud rendering system generates the shadow map according to the pre-computed results, and for the shadows generated by the key light sources, ensure high quality and high resolution; for other light sources, lower resolution may be used.

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

[0014] S9: Render execution; that is, according to the optimized pre-computed 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, which are feedbacks for this rendering to facilitate users to further adjust rendering parameters and other data.

[0017] In step S10, it also includes; the user views the rendering result through the user end, and the user downloads or further processes through the user end.

[0018] In step S9, it also includes: during real-time rendering, if the light source in the scene changes, the cloud rendering system automatically re-performs light source clustering and adjustment of the pre-computed strategy, that is, enters step S4.

[0019] In step S8, the shadow synthesis is a mixing, blurring, and sharpening process of the shadow map to ensure visual coherence and realism.

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

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

[0022] The present application can enhance image quality: while maintaining high-quality shadow effects, avoid excessive computation and performance degradation caused by a large number of light sources.

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

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

[0025] The present application 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 the challenges that traditional single pre-computation methods cannot solve.

[0026] The present application can flexibly cope with changes: during real-time rendering, the pre-computation strategy is dynamically adjusted according to the scene to adapt to changes in light sources and environment.

[0027] The present application can expand the application range of technology: by providing an extensible shadow pre-computation optimization method, it is suitable for various scale offline rendering platforms and different application scenarios.

[0028] The present application can reduce the operation difficulty: automatic clustering and pre-computation process, reducing the need for user manual adjustment, reducing the operation difficulty and technical threshold.

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

[0030] 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.

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

[0032] 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.

[0033] Adaptability enhancement: the method can adapt to different rendering environments and scene changes, especially for dynamically changing light sources and complex scenes, providing flexible processing strategies.

[0034] User experience optimization: speeding up the rendering time provides faster feedback for users, while simplifying the operation process, as the automated clustering and pre-computation reduce the need for user manual adjustment.

[0035] Scalability: the technology can be easily integrated into existing rendering systems, whether in small-scale personal studios or large-scale rendering farms.

[0036] Intelligent level improvement: intelligent decision support is introduced, such as automatic identification of key light sources and automatic adjustment of pre-computation parameters, improving the intelligent level of the rendering system.

[0037] Compatibility and maintainability: due to the adoption of standardized clustering algorithms and pre-computation techniques, the method is easy to maintain and upgrade, and is also compatible with multiple file formats and rendering engines.

[0038] Overall, the shadow pre-computation optimization method based on light source clustering technology technically realizes the dual improvement of 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. BRIEF DESCRIPTION OF DRAWINGS

[0039] Figure 1 The method flowchart of the present application. DETAILED DESCRIPTION

[0040] The present application will be described in detail below in combination with the drawings and specific embodiments.

[0041] Please refer to Figure 1 The present application provides a method for optimizing shadow pre-computation based on light source clustering technology, comprising the following steps.

[0042] S1: The user uploads or specifies the scene to be rendered on the user end, which contains the geometric data, material information and light source configuration of the scene.

[0043] S2: The user end 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 the rendering parameters on the user end and submits the scene file and rendering configuration to the cloud rendering system (cloud rendering farm).

[0045] S4: Through the script processing of the node machine of the cloud rendering system, the light sources are clustered; the specific way of light source clustering is to use clustering algorithms (such as K-means, DBSCAN, etc.) to group the light sources, and to gather the light sources with similar attributes together.

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

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

[0048] S7: After the allocation of computing power, the texture map is generated; that is, the cloud rendering system generates shadow map according to the pre-computation result, and ensures high quality and high resolution for the shadow generated by the key light source; for other light sources, lower resolution may be used.

[0049] S8: Shadow synthesis; all light source shadow maps are synthesized into the final shadow effect.

[0050] S9: Rendering execution; that is, according to the optimized pre-computed parameters, the cloud rendering system performs the final rendering process to generate images or animations 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 end, and the user downloads or further post-processing through the user end.

[0054] In step S9, it also includes: during real-time rendering, if the light source in the scene changes, the cloud rendering system automatically re-performs light source clustering and adjustment of pre-computation strategy, that is, enters step S4.

[0055] In step S8, the shadow synthesis is a mixing, blurring, sharpening process of the shadow map to ensure visual coherence and realism.

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

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

[0058] The present application uses 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 application identifies the key light sources that contribute most to the scene shadows in each cluster. These key light sources will have a decisive influence on the final shadow quality.

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

[0061] The present application synthesizes all light source pre-computed shadow maps into the final shadow effect. This may require the use of specific mixing, blurring or other synthesis techniques to ensure visual coherence and realism.

[0062] The present application dynamically adjusts light source clustering and pre-computation strategies based on real-time rendering requirements. If the light sources in the scene change, the system can automatically re-cluster and adjust the pre-computation parameters.

[0063] The present application implements a performance monitoring system to evaluate the effectiveness of the optimized shadow pre-computation method, including rendering time and shadow quality. According to the monitoring results, adjust the clustering parameters and pre-computation strategy to achieve the best performance and quality balance.

[0064] Through the above solution, the rendering efficiency can be significantly improved while ensuring the quality of the shadow, 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, which helps to improve the overall performance and user experience of rendering tasks.

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

[0066] 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.

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

[0068] Image quality improvement: Despite reducing the amount of computation, the final output shadow effect still maintains high quality and realism due to accurate processing of key light sources and intelligent synthesis techniques.

[0069] Adaptability enhancement: The method can adapt to different rendering environments and scene changes, especially for dynamically changing light sources and complex scenes, providing flexible processing strategies.

[0070] User experience optimization: Accelerating rendering time provides faster feedback for users, while simplifying the operation process as automated clustering and pre-computation reduce the need for manual adjustments.

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

[0072] 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.

[0073] Compatibility and maintainability: As standardized clustering algorithms and pre-computation techniques are used, the method is easy to maintain and upgrade, and is also compatible with various file formats and rendering engines.

[0074] Overall, the shadow pre-computation optimization method based on light source clustering technology technically realizes the dual improvement of 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.

[0075] The above is only the preferred embodiment of the present application, and is not intended to limit the present application. Any modification, equivalent replacement and improvement made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for optimizing shadow precomputation based on light source clustering technique, characterized in that, The method comprises the following steps: S1: the user uploads or specifies a scene file that needs to be rendered on the user end, which contains the geometric data, material information and light source configuration of the scene; S2: the user end 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 the rendering parameters on the user end and submits the scene file and rendering configuration to the cloud rendering system; S4: the light sources are clustered through the script processing of the node machines of the cloud rendering system; the specific way of light source clustering is to use a clustering algorithm to group the light sources and gather the light sources with similar attributes together; S5: key light source identification is performed; that is, for each cluster, the system identifies the key light source according to the influence degree of the light source on the scene; S6: machine resource allocation and pre-computation are performed; that is, for the key light source, the system allocates more computing resources for standard shadow pre-computation; for the non-key light source, low resolution is used for pre-computation; S7: a map is generated after the allocation of computing power; That is, the cloud rendering system generates a shadow map according to the pre-computation result; for the shadow generated by the key light source, high quality and high resolution are ensured; for the shadow generated by other light sources, lower resolution is used; S8: shadow synthesis is performed; the shadow maps of all light sources are synthesized into the final shadow effect; S9: rendering is performed; That is, according to the optimized pre-computation parameters, the cloud rendering system performs the final rendering process to generate an image or animation with optimized shadow; S10: the cloud rendering system generates a rendering output image and outputs it to the user end.

2. The method for optimizing shadow precomputation 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 precomputation based on light source clustering technology according to claim 1, characterized in that, In step S10, it also includes that the user views the rendering result through the user end, and the user downloads or further processes the result through the user end.

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

5. The method for optimizing shadow precomputation based on light source clustering technology according to claim 1, characterized in that, In step S8, the shadow synthesis is a mixing, blurring and sharpening process of the shadow map to ensure visual coherence and realism.

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

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