Sparse Volumetric Path Planning for Memory-Efficient 3D Geometry
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Solution Overview
Problem
Current computing systems face challenges in processing large 3D data sets 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
The implementation of a sparse volumetric data structure, such as a Sparse Sexaquaternary Tree (SST), that allows for the representation of 3D objects using voxels, where empty space is culled, reducing storage and processing requirements, and enabling real-time updates in 3D rendering systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If dense volumetric data structures are used to represent 3D environments, then complete spatial information is available, but memory consumption and processing time increase significantly
Solution Approach 1:
The patent applies local quality by differentiating between occupied and empty voxels in the volumetric data structure. Only occupied voxels (those containing actual environmental data) 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 volumetric space is segmented into individual voxels that can be independently evaluated for occupancy. This segmentation allows the system to process and store only the necessary portions of the 3D environment (occupied voxels) rather than treating the entire volume as a single dense data structure, thereby reducing memory consumption while preserving essential spatial information.
2Reliability
If complete volumetric data is processed in real-time, then accurate path planning is achieved, but processing speed decreases due to computational complexity
Solution Approach 1:
The patent extracts and processes only the occupied voxels from the complete volumetric data set, removing unnecessary empty space calculations from the processing pipeline. This extraction approach maintains accurate path planning by focusing computational resources on actual environmental obstacles and features while eliminating wasted computation on empty regions, thereby improving processing speed without sacrificing accuracy.
Solution Approach 2:
The system performs partial action by processing only the subset of voxels that are actually occupied rather than the entire volumetric data set. This partial processing approach is sufficient for accurate path planning since empty voxels do not contain environmental features that would affect navigation, thus achieving the required accuracy with reduced computational complexity and improved processing speed.
3Reliability
If high frame rate rendering is maintained, then user experience quality is preserved, but power consumption increases
Solution Approach 1:
The rendering system applies local quality by updating and rendering only the occupied voxels that contain environmental information, rather than processing the entire volumetric data set at every frame. This approach preserves user experience quality by maintaining accurate representation of actual environmental features while significantly reducing the computational workload and power consumption associated with rendering empty space repeatedly.
4Reliability
If latency is reduced to prevent motion sickness, then user comfort is improved, but processing resources must be allocated differently
Solution Approach 1:
The patent extracts only the essential occupied voxel data needed for immediate rendering and path planning, removing unnecessary empty voxel processing from the critical path. This extraction enables reduced latency by minimizing the data volume that must be processed for each frame, thereby improving user comfort and preventing motion sickness while simplifying processing resource allocation compared to handling complete dense volumetric data.
Data Source
AI summary
A view of geometry captured in image data generated by an imaging sensor is compared with a description of the geometry in a volumetric data structure. The volumetric data structure describes the volume at a plurality of levels of detail and includes entries describing voxels defining subvolumes of the volume at multiple levels of detail. The volumetric data structure includes a first entry to describe voxels at a lowest one of the levels of detail and further includes a number of second entries to describe voxels at a higher, second level of detail, the voxels at the second level of detail representing subvolumes of the voxels at the first level of detail. Each of these entries include bits to indicate whether a corresponding one of the voxels is at least partially occupied with the geometry. One or more of these entries are used in the comparison with the image data.