3D Point Cloud Hash Mapping for Fast Active Voxel Access
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Solution Overview
Problem
Existing systems face challenges in efficiently processing 3D data for applications like autonomous driving and 3D graphics due to high latency and energy consumption in accessing and creating metadata for 3D point clouds, particularly in systems with high-dimensional data structures.
Innovation Solution
A hardware accelerator and instruction set are developed to create efficient N-dimensional to one-dimensional hash-maps and metadata, utilizing 3D data format-aware hashing and hierarchical metadata storage for faster access to active voxel grids, reducing latency and energy consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If traditional software-based hash-map and metadata creation methods are used, then processing accuracy is maintained, but processing time exceeds 100 milliseconds and energy consumption is high
Solution Approach 1:
The patent replaces software-based hash-map and metadata creation mechanisms with dedicated hardware circuitry. The hardware accelerator includes specialized units for hash computation, voxel grid processing, and metadata generation that operate in parallel, reducing processing time from over 100 milliseconds to less than 6 milliseconds while maintaining processing accuracy.
Solution Approach 2:
The hardware accelerator is designed to autonomously perform hash-map creation and metadata generation without requiring software intervention. The system includes self-contained units that automatically compute hashes, identify active voxels, generate adjacency information, and create metadata structures independently, enabling real-time processing of 3D point cloud data.
2Use of energy by moving object
If high-dimensional 3D point cloud data is processed using conventional methods, then data accuracy is preserved, but energy consumption increases significantly
Solution Approach 1:
The hardware accelerator divides the 3D point cloud processing into distinct functional segments: hash computation units, voxel grid processing units, adjacency map generation units, and metadata creation units. Each segment handles specific tasks in parallel, reducing overall energy consumption while maintaining real-time processing capability for high-dimensional data.
Solution Approach 2:
The patent transforms N-dimensional voxel coordinates into one-dimensional hash values through specialized hardware that computes hash functions in parallel. This dimensional transformation enables efficient storage and retrieval of 3D point cloud data while reducing the energy required for metadata creation and adjacency information generation.
Data Source
AI summary
An embodiment of an apparatus comprises a hardware accelerator to perform a three-dimensional (3D) point cloud data access operation, and circuitry coupled to the hardware accelerator to control the hardware accelerator to perform the 3D point cloud data access operation in response to a request. Other embodiments are disclosed and claimed.