Voxel Generation with Hash Tables for Point Cloud Efficiency
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Solution Overview
Problem
Generating voxel representations of objects based on point cloud representations requires significant computing resources, which can be inefficient and resource-intensive.
Innovation Solution
Utilizing a hash table to correlate point locations with voxel indications, allowing processors to efficiently generate voxels by calculating mean feature values without checking memory locations for non-generated voxels, thereby reducing computational load.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional methods are used to generate voxel representations from point clouds, then accurate voxel representation is achieved, but significant computing resources are consumed
Solution Approach 1:
The patent pre-calculates and stores voxel indications in a hash table during point cloud processing, so that when generating the final voxel representation, the system only needs to retrieve pre-computed information rather than performing comprehensive computations for each voxel. This preliminary action significantly reduces the computational resources required during the actual voxel generation phase while maintaining representation accuracy.
Solution Approach 2:
The patent introduces a hash table as an intermediary data structure that correlates point locations with voxel indications. This intermediary mechanism allows the system to efficiently map between point cloud data and voxel representations without directly computing all possible voxel values, thereby reducing computing resource consumption while preserving accurate voxel representation through the intermediary mapping relationship.
2Reliability
If comprehensive memory checking is performed to ensure accurate voxel generation, then representation accuracy is maintained, but computational load increases
Solution Approach 1:
The patent extracts and stores only the necessary voxel indications in the hash table during point cloud processing, rather than computing and storing all possible voxel information. This extraction approach allows the system to retrieve only the required voxel data during generation, avoiding the computational load of checking all memory locations while maintaining accuracy for the voxels that are actually needed.
Solution Approach 2:
The system performs preliminary computation of voxel indications during point cloud processing and stores them in the hash table. This preliminary action eliminates the need for comprehensive memory checking during the actual voxel generation phase, significantly improving processing efficiency while maintaining representation accuracy through the pre-computed voxel indications.
Data Source
AI summary
Apparatuses, systems, and techniques are to convert a point cloud into voxels. In at least one embodiment, a processor causes a point cloud representation of an environment and/or objects to be represented as voxels based, at least in part, on a data structure that uses point locations to indicate voxels.