Sparse Voxel Raycasting for Low-Latency Computer Vision
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Solution Overview
Problem
Current computing systems face challenges in processing and rendering large volumetric data sets efficiently, particularly in augmented, virtual, 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) format, that allows for efficient storage and processing of 3D data by tagging voxels as occupied or empty, enabling the removal of empty space and utilizing hardware acceleration for rapid updates in 3D renderers, thereby reducing latency and improving processing speed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a separate graphics processing unit (GPU) and computer vision subsystem are used in parallel, then processing capability is improved, but device complexity and power consumption increase
Solution Approach 1:
The patent combines the computer vision subsystem and graphics processing unit into a single integrated processor. The processor includes a vision processing pipeline with depth estimation networks, voxelization units, and a rendering pipeline with rasterization units and depth buffer management, all unified in one device rather than using separate GPU and CV subsystems.
Solution Approach 2:
The integrated processor performs multiple functions including depth estimation from RGB images, voxelization of 3D space, rendering of virtual objects, and composite image generation. This multi-functional approach replaces the need for separate specialized hardware components.
2Measurement precision
If dense volumetric data structures are used to represent 3D space, then processing accuracy is improved, but memory requirements and processing time increase
Solution Approach 1:
The patent extracts and processes only the relevant portions of 3D space by using depth estimation to identify occupied voxels and empty space removal to eliminate unoccupied regions. This selective processing approach maintains accuracy for visible regions while reducing overall memory requirements.
Solution Approach 2:
The system applies different processing quality levels to different regions of 3D space. Occupied voxels containing visual information are processed with high accuracy, while empty spaces are removed or represented with minimal data, optimizing the balance between precision and memory usage.
3Adaptability or versatility
If depth estimation and voxelization are performed in software, then flexibility is improved, but processing speed and real-time performance decrease
Solution Approach 1:
The patent replaces software-based depth estimation and voxelization with dedicated hardware circuits including depth estimation networks, voxelization units, and rendering pipelines implemented in silicon. This hardware implementation provides both the flexibility of programmable logic and the speed of fixed-function circuits.
Data Source
AI summary
A raycaster performs a raycasting algorithm, where the raycasting algorithm takes, as an input, a sparse hierarchical volumetric data structure. Performing the raycasting algorithm includes casting a plurality of rays from a reference point into the 3D volume, and, for each of the plurality of rays, traversing the ray to determine whether voxels in the set of voxels are intersected by the ray and are occupied, where the ray is to be traversed according to an approximate traversal algorithm.