Adaptive 3D Gaussian Coefficients for Efficient Scene Rendering
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Solution Overview
Problem
The high memory consumption and computational complexity of 3D Gaussian (3DG) splatting for 3D scene rendering, particularly in consumer devices and low-end GPUs, due to the large number of coefficients required for spherical harmonics, especially for specular surfaces, hinder its deployment and efficiency.
Innovation Solution
Adaptive computation of the number of coefficients for each 3D Gaussian or group of 3DGs based on the scene's material properties, using lower degree harmonics for Lambertian surfaces and higher degree harmonics for specular surfaces, reducing memory footprint and computational load.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If higher degree spherical harmonics are used for all surfaces, then rendering quality for specular surfaces is improved, but memory consumption and computational complexity increase
Solution Approach 1:
The patent applies different degrees of spherical harmonics to different surfaces based on their material properties. Specular surfaces use higher degree harmonics (e.g., degree 4 or 8) to capture accurate reflections, while Lambertian surfaces use lower degree harmonics (e.g., degree 0-2) to reduce computational load. This local differentiation resolves the contradiction by allocating computational resources only where needed for high rendering quality.
Solution Approach 2:
The patent dynamically adjusts the degree parameter of spherical harmonics based on surface material classification. By changing this parameter adaptively rather than using a fixed high degree for all surfaces, the system maintains high rendering quality for specular surfaces while significantly reducing memory consumption and computational complexity for Lambertian surfaces.
2Measurement precision
If higher degree spherical harmonics are used for all surfaces, then rendering quality for specular surfaces is improved, but computational complexity increases
Solution Approach 1:
The patent applies different degrees of spherical harmonics to different surfaces based on their material properties. Specular surfaces use higher degree harmonics (e.g., degree 4 or 8) to capture accurate reflections, while Lambertian surfaces use lower degree harmonics (e.g., degree 0-2) to reduce computational load. This local differentiation resolves the contradiction by allocating computational resources only where needed for high rendering quality.
Solution Approach 2:
The patent dynamically adjusts the degree parameter of spherical harmonics based on surface material classification. By changing this parameter adaptively rather than using a fixed high degree for all surfaces, the system maintains high rendering quality for specular surfaces while significantly reducing memory consumption and computational complexity for Lambertian surfaces.
3Ease of manufacture
If uniform number of coefficients is used for all Gaussians, then implementation simplicity is maintained, but memory requirements increase unnecessarily
Solution Approach 1:
The patent applies different degrees of spherical harmonics to different surfaces based on their material properties. Specular surfaces use higher degree harmonics (e.g., degree 4 or 8) to capture accurate reflections, while Lambertian surfaces use lower degree harmonics (e.g., degree 0-2) to reduce computational load. This local differentiation resolves the contradiction by allocating computational resources only where needed for high rendering quality.
Solution Approach 2:
The patent segments the scene into different material types (specular and Lambertian surfaces) and applies different coefficient configurations to each segment. This segmentation allows the system to use fewer coefficients for Lambertian surfaces while maintaining sufficient accuracy, thereby reducing overall memory requirements without significantly complicating the implementation.
Data Source
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AI summary
Methods and apparatus are provided to implement adaptive attributes for three-dimensional Gaussians for image processing. The embodiments enable image rendering, encoding, decoding and other processes to be performed using the three-dimensional Gaussians. In at least one embodiment, the number of coefficients for each Gaussian or each group of Gaussians is adapted to a scene. In other embodiments, the number of parameters for each three-dimensional Gaussian or each group of three-dimensional Gaussians is stored and/or transmitted.