Sparse Volumetric Voxel Hierarchy for Low-Latency 3D Rendering
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Solution Overview
Problem
Current computing systems face challenges in processing and rendering high-frame rate, high-resolution 3D data for augmented and mixed reality applications due to constraints in memory, processing power, and latency, particularly in devices like drones and wearable devices, which require efficient handling of volumetric data to prevent motion sickness and ensure smooth graphical presentations.
Innovation Solution
The implementation of a sparse volumetric data structure that uses a recursive subdivision of voxels, 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 volumetric data, while also providing hardware acceleration for real-time updates in 3D rendering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If dense volumetric data structures are used for high-resolution 3D rendering, then rendering quality is improved, but memory consumption and processing time increase significantly
Solution Approach 1:
The volumetric data is segmented into a hierarchical structure of voxels organized in a sexaquaternary tree, where the volume is recursively divided into smaller sub-volumes. This segmentation allows the system to process and store only the necessary portions of the volume at each level of detail, reducing overall memory consumption while maintaining rendering quality where needed.
Solution Approach 2:
The sparse volumetric data structure applies local quality by storing detailed voxel information only in regions where geometry actually exists, while using coarser representations or empty space indicators in regions without geometry. This allows high rendering quality in occupied regions while minimizing memory usage in empty regions.
2Manufacturing precision
If high-resolution volumetric data is processed in real-time, then rendering quality is improved, but processing speed decreases due to computational complexity
Solution Approach 1:
The hierarchical voxel structure segments the volumetric data into manageable levels of detail, allowing the processing system to work with smaller data subsets at each level. This reduces computational complexity while maintaining the ability to achieve high rendering quality when needed by processing only relevant portions of the segmented data.
Solution Approach 2:
The system performs partial processing by focusing computational resources only on voxels that contain geometry or are visible from the current viewpoint, rather than processing the entire volumetric dataset. This partial action approach maintains processing speed while still achieving high rendering quality for the visible portions.
3Measurement precision
If complete volumetric data is transmitted for rendering, then rendering accuracy is improved, but data transmission time and bandwidth requirements increase
Solution Approach 1:
The sparse volumetric data structure extracts and transmits only the essential voxel information that contributes to the final rendering, omitting redundant data representing empty space. This extraction process reduces data transmission time and bandwidth requirements while maintaining rendering accuracy by preserving all geometrically relevant information.
Solution Approach 2:
The transmission process applies local quality by sending detailed voxel data only for regions containing geometry, while using compressed or omitted representations for empty regions. This approach reduces overall data transmission time while maintaining rendering accuracy in the important occupied regions.
4Adaptability or versatility
If separate GPU and computer vision subsystems are used in parallel, then functional versatility is improved, but system complexity and latency increase
Solution Approach 1:
The patent merges the computer vision processing and graphics rendering operations by using the same sparse volumetric data structure for both depth image generation and 3D scene rendering. This unified approach eliminates the need for separate parallel subsystems, reducing system complexity and latency while maintaining functional versatility through the multi-purpose use of the volumetric data.
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.