Sparse Volumetric Voxel Structure 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 allows for the subdivision of volumetric representations, tagging voxels as occupied or empty, and utilizing a sparse sexaquaternary tree format to reduce storage and processing requirements, enabling faster data processing and transmission, and integrating hardware acceleration for real-time updates in 3D rendering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If dense volumetric data structures are used to represent 3D space in AR/VR systems, then complete spatial coverage and accuracy are achieved, but memory requirements and processing load increase significantly
Solution Approach 1:
The volumetric space is divided into discrete voxels that can be independently tagged as occupied or empty. This segmentation allows the system to represent only the necessary portions of 3D space rather than maintaining dense data structures for entire volumes, reducing memory requirements while preserving spatial accuracy where needed.
Solution Approach 2:
Different regions of volumetric space are treated differently based on their importance. Occupied voxels containing objects or geometry receive detailed representation and processing, while empty voxels are efficiently culled or represented with minimal data. This local quality approach optimizes the balance between spatial accuracy and memory usage by applying different data densities to different spatial regions.
2Manufacturing precision
If high-resolution 3D volumetric data is processed in real-time for AR/VR rendering, then graphical quality and immersion are improved, but processing time and latency increase
Solution Approach 1:
Empty or irrelevant voxels are extracted and removed from the active processing pipeline. The system identifies and culls voxels that do not contain geometry or objects, preventing them from consuming processing resources. This extraction of unnecessary data elements enables real-time processing of high-resolution volumetric data by focusing computational effort only on occupied regions.
Solution Approach 2:
Voxels are pre-tagged as occupied or empty during data ingestion or scene construction, before rendering occurs. This preliminary classification allows the rendering pipeline to skip processing of empty voxels entirely, reducing real-time processing latency while maintaining high graphical quality for occupied regions. The tagging structure enables efficient query and traversal during rendering operations.
3Measurement precision
If complete volumetric data is transmitted for remote rendering or cloud processing, then rendering accuracy is maintained, but bandwidth requirements and transmission time increase
Solution Approach 1:
Only occupied voxels containing relevant geometric or semantic information are extracted and transmitted to remote systems or cloud rendering services. Empty voxels are identified and excluded from the transmission stream, significantly reducing bandwidth requirements and transmission time while preserving rendering accuracy for the transmitted data portions.
4Productivity
If separate GPU and computer vision subsystems are used in parallel, then specialized processing capabilities are achieved, but system complexity and inter-subsystem synchronization overhead increase
Solution Approach 1:
The patent integrates computer vision processing and graphics rendering operations to work on unified volumetric data structures rather than maintaining separate parallel pipelines. By merging the processing approaches and using common data representations (occupied/empty voxel tags), the system reduces inter-subsystem synchronization overhead and architectural complexity while maintaining the specialized processing capabilities of both GPU and vision subsystems.
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.