Sparse Volumetric Data Structure for Low-Latency 3D Rendering
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Solution Overview
Problem
Existing computing systems struggle to efficiently process large 3D data sets required for augmented, virtual, and mixed reality applications due to memory and processing constraints, leading to latency issues that can cause user discomfort.
Innovation Solution
Implementing a sparse volumetric data structure, such as a sparse sexaquaternary tree format, to represent objects, allowing for the culling of empty space and reducing storage and processing demands, combined with hardware acceleration for real-time updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If dense volumetric data structures are used to store 3D data, then high-resolution rendering is achieved, but memory usage and processing requirements increase significantly
Solution Approach 1:
The patent applies local quality by differentiating between occupied and empty voxels, storing detailed information only where necessary (in occupied regions) while using minimal representation for empty space. This allows high-resolution rendering in regions of interest while reducing overall memory consumption through selective detail preservation.
Solution Approach 2:
The sparse volumetric data structure creates a porous data representation by explicitly identifying and separating empty regions from occupied regions. This porous structure allows the system to maintain high-resolution details in occupied voxels while efficiently skipping over empty space, thereby reducing memory usage without sacrificing rendering quality in relevant areas.
2Reliability
If complete volumetric processing is performed, then accurate collision detection is achieved, but processing time increases causing latency
Solution Approach 1:
The patent extracts and removes empty voxels from the processing pipeline, creating a sparse representation that excludes unnecessary computational work. By taking out empty space from the volumetric data structure, the system performs collision detection only on occupied regions, maintaining accuracy while significantly reducing processing time and latency.
Solution Approach 2:
The system applies partial action by performing complete volumetric processing only on occupied regions rather than the entire volume. This selective processing approach maintains collision detection accuracy for relevant objects while avoiding excessive computation on empty space, thereby reducing overall processing time and latency.
3Productivity
If high-frame rate rendering is implemented, then smooth visual experience is achieved, but processing power requirements increase
Solution Approach 1:
The patent segments the volumetric data into occupied and empty regions, allowing the rendering pipeline to process only relevant portions at high frame rates. This segmentation enables smooth visual experience in occupied regions while reducing overall processing power requirements by excluding empty space from frame-by-frame updates.
Data Source
AI summary
A volumetric data structure models a particular volume representing the particular 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 first level of detail, the first level of detail includes the lowest level of detail in the volumetric data structure, values of the first set of bits indicate whether a corresponding one of the voxels is at least partially occupied by respective geometry, where the volumetric data structure further includes a number of second entries representing voxels at a second level of detail higher than the first level of detail, the voxels at the second level of detail represent subvolumes of volumes represented by voxels at the first level of detail, and the number of second entries corresponds to a number of bits in the first set of bits with values indicating that a corresponding voxel volume is occupied.