Sparse Volumetric Hierarchy for Low-Latency Occupancy Updates
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Solution Overview
Problem
Current computer systems face challenges in processing and rendering large volumes of 3D data efficiently, particularly in augmented and mixed reality applications, due to constraints in memory, processing resources, and power, which can lead to latency issues and motion sickness in users.
Innovation Solution
A sparse volumetric data structure, such as a sparse sexaquaternary tree, is introduced to unify computer vision and 3D rendering, allowing for recursive subdivision of volumetric representations and tagging voxels as occupied or empty, reducing storage and processing requirements, and enabling hardware acceleration for real-time updates.
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 consumption and processing time increase significantly
Solution Approach 1:
The patent segments the volumetric space into discrete voxels organized in a hierarchical tree structure, where each node represents a volumetric region that can be independently processed and stored, allowing selective refinement of only necessary regions
Solution Approach 2:
The patent applies different levels of detail to different regions of the volumetric space based on their importance and occupancy, using occupancy encoding to store only regions that contain relevant data, thereby reducing overall memory consumption while maintaining accuracy where needed
2Measurement precision
If high-resolution volumetric data is processed in real-time for AR/VR rendering, then visual quality is maintained, but processing speed decreases due to computational complexity
Solution Approach 1:
The patent implements dynamic level-of-detail adjustment in the volumetric data structure, allowing the system to adaptively refine or coarsen voxel representations based on current rendering requirements, viewer position, and scene importance, enabling real-time processing at appropriate quality levels
Solution Approach 2:
By segmenting the volumetric data into a hierarchical tree structure, the patent enables independent processing of different spatial regions at different resolutions, allowing critical regions to be processed at high detail while non-critical regions use coarser representations, thus maintaining visual quality where needed while improving overall processing speed
3Reliability
If complete volumetric updates are performed frequently to maintain real-time AR/VR scene accuracy, then spatial data freshness is improved, but power consumption and latency increase
Solution Approach 1:
The patent extracts and updates only the occupied regions of the volumetric space that contain relevant scene data, rather than performing blanket updates across the entire volume. The occupancy encoding identifies and isolates only the necessary regions for updating, reducing computational workload and power consumption while maintaining data freshness for relevant areas
Solution Approach 2:
The patent performs partial updates by selectively refreshing only the portions of the volumetric data that have changed or are currently visible and relevant, rather than performing complete volumetric updates. This partial action approach maintains acceptable spatial data freshness while significantly reducing the energy cost and latency of update operations
4Measurement precision
If detailed volumetric representations are used to enable accurate occlusion and lighting calculations, then rendering accuracy is improved, but device complexity and computational resources required increase
Solution Approach 1:
The patent segments the volumetric scene into a hierarchical tree structure where occlusion and lighting calculations can be performed efficiently by traversing only the relevant branches of the tree. This segmentation allows the system to avoid unnecessary calculations in empty or distant regions while maintaining accurate rendering in visible and important areas
Solution Approach 2:
The patent applies high-detail volumetric representations and associated rendering calculations only to regions that are currently visible and important to the scene, while using coarser representations for distant or occluded regions. This local quality approach maintains rendering accuracy where it matters most while reducing overall computational resource requirements
Data Source
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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.