Sparse Volumetric Rendering for Low-Latency 3D Scene Processing
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Solution Overview
Problem
Current computer systems face challenges in processing large 3D data sets efficiently, particularly in augmented and mixed reality applications, due to constraints in memory and processing resources, leading to latency issues and motion sickness in users.
Innovation Solution
The implementation of a sparse volumetric data structure that tags voxels as occupied or empty, allowing for the removal of empty space and utilizing a sparse sexaquaternary tree format, which reduces storage requirements and enables faster processing and transmission of data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If dense volumetric data structures are used to represent 3D scenes, then complete spatial information is preserved, but memory consumption and processing time increase significantly
Solution Approach 1:
The patent applies local quality by differentiating between occupied and empty voxels, assigning different properties to different regions of the volumetric space. Only occupied voxels (those containing actual scene geometry) are stored and processed, while empty voxels are implicitly represented. This allows the system to maintain complete spatial information where needed while avoiding storage of redundant empty space, directly resolving the contradiction between information completeness and memory consumption.
Solution Approach 2:
The patent extracts and removes empty space from the volumetric data structure by using a sparse representation that only includes occupied voxels. The system identifies and eliminates redundant empty voxels from the data structure, keeping only the essential occupied voxels that represent actual scene content. This extraction process reduces memory consumption while preserving all necessary spatial information for rendering and processing.
2Manufacturing precision
If complete volumetric data is processed in real-time, then rendering accuracy is maintained, but processing speed decreases due to latency
Solution Approach 1:
The patent extracts only the essential occupied voxels from the complete volumetric data, removing redundant empty space that would otherwise need to be processed. This extraction enables the system to maintain rendering accuracy by preserving all occupied voxels while significantly reducing the total number of voxels that require processing, thereby improving processing speed and reducing latency in real-time applications.
Solution Approach 2:
The patent applies partial action by processing only the necessary subset of volumetric data (occupied voxels) rather than the complete dataset. The system performs rendering and processing operations on a partial representation that includes only occupied voxels, which is sufficient to maintain rendering accuracy while dramatically reducing processing time and latency for real-time performance.
3Extent of automation
If traditional parallel GPU and computer vision subsystems are used, then rendering and vision tasks can run simultaneously, but system complexity and resource requirements increase
Solution Approach 1:
The patent merges the computer vision pipeline and graphics rendering pipeline into a unified system that shares the same volumetric data structure (occupied/empty voxel tags). Instead of maintaining separate parallel subsystems with independent data representations, the system combines both functions to operate on a single sparse volumetric representation, reducing system complexity while maintaining parallel processing capabilities through shared hardware resources.
Data Source
AI summary
A ray is cast into a volume described by a volumetric data structure, which describes the volume at a plurality of levels of detail. A first entry in the volumetric data structure includes a first set of bits representing voxels at a lowest one of the plurality of levels of detail, and values of the first set of bits indicate whether a corresponding one of the voxels is at least partially occupied by respective geometry. A set of second entries in the volumetric data structure describe voxels at a second level of detail, which represent subvolumes of the voxels at the first lowest level of detail. The ray is determined to pass through a particular subset of the voxels at the first level of detail and at least a particular one of the particular subset of voxels is determined to be occupied by geometry.