3D Gaussian Over-Segmentation for Low-Memory Scene Rendering
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Solution Overview
Problem
The high memory consumption of 3D Gaussians (3DGs) in 3D scene rendering poses a bottleneck for deployment on consumer devices and low-end GPUs, complicating transmission and manipulation, despite their potential for faster rendering compared to Neural Radiance Fields (NeRFs).
Innovation Solution
Implement over-segmentation of 3DGs to group Gaussians that are radiometrically and geometrically homogeneous, using methods like simultaneous optimization and octree-based updates to reduce memory requirements and simplify manipulation, while maintaining rendering quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If 3D Gaussians are used for 3D scene rendering, then rendering speed is improved compared to NeRFs, but memory consumption increases significantly
Solution Approach 1:
The patent applies segmentation by dividing the set of 3D Gaussians into multiple clusters or groups based on spatial proximity and radiometric similarity. Each cluster is represented by a representative Gaussian, reducing the total number of Gaussians that need to be stored and processed. This segmentation maintains rendering quality while significantly reducing memory consumption by grouping homogeneous Gaussians together.
2Manufacturing precision
If the number of 3D Gaussians is increased for better rendering quality, then rendering accuracy is improved, but device complexity increases making deployment on consumer devices difficult
Solution Approach 1:
The patent merges multiple 3D Gaussians that are spatially close and radiometrically similar into single representative Gaussians or clusters. This merging process reduces the total number of Gaussian objects while preserving the collective rendering contribution of the original Gaussians. The result is a simplified system with fewer objects to manage, reducing device complexity while maintaining rendering quality.
3Productivity
If 3D Gaussians are used instead of NeRFs, then rendering efficiency is improved, but ease of manipulation and transmission deteriorates
Solution Approach 1:
By segmenting 3D Gaussians into clusters with representative objects, the patent creates a hierarchical structure that is easier to manipulate. Instead of handling thousands of individual Gaussians, users and algorithms can work with a smaller set of cluster representatives. This segmentation also facilitates transmission by allowing selective transmission of only relevant clusters or their representatives, improving ease of operation while preserving rendering efficiency.
Data Source
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AI summary
Methods and apparatus are provided to perform over-segmentation of three-dimensional Gaussians to greatly reduce the complexity of subsequent image processing. The over-segmentation process creates super three-dimensional Gaussians to simplify 3DG manipulation. Three-dimensional Gaussians belonging to same segments are radiometrically and/or geometrically homogenous. Further processing, such as compression using intra or inter prediction are performed on three-dimensional Gaussian representatives.