3D Data Dense Field for Machine Vision Processing
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Solution Overview
Problem
Existing machine vision techniques face inefficiencies when processing three-dimensional data, particularly in searching for neighboring points in 3D data, which leads to significant processing time consumption and reduced performance due to interrupting parallelization with conditional branches.
Innovation Solution
The approach involves converting three-dimensional data into a densely-populated field where each cell has associated data, allowing for the avoidance of searching for neighboring points and reducing processing time by using field values consecutively, thereby improving performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional 3D data processing techniques are used to search for neighboring points, then the search can be performed on the original data structure, but the processing time increases significantly and parallelization is interrupted by conditional branches
Solution Approach 1:
The patent applies preliminary action by pre-processing the 3D data into a dense field representation before the actual pattern search. The three-dimensional data is converted into a dense field where each cell contains pre-computed information about its neighbors, eliminating the need for runtime neighbor searches. This pre-computation allows the pattern recognition process to directly query pre-processed data without conditional branches, significantly reducing processing time while maintaining recognition accuracy.
2Productivity
If traditional 3D data processing techniques are used, then the original data structure is maintained, but processing efficiency decreases due to conditional branches interrupting parallelization
Solution Approach 1:
The patent applies parameter changes by transforming the data representation from sparse point cloud format to a dense field structure. The conversion involves changing how spatial data is organized - from discrete points requiring neighbor searches to a continuous field where each cell has pre-computed properties. This parameter transformation enables efficient parallel processing because the dense field structure allows uniform memory access patterns without conditional branches, improving processing efficiency despite the increased data structure complexity during conversion.
3Speed
If a dense field is generated from 3D data, then processing speed increases by avoiding neighbor searches, but the conversion process requires additional computational steps
Solution Approach 1:
The conversion process complexity is justified by the preliminary action it performs. By converting 3D data to a dense field representation in advance, the system pre-computes all neighbor relationships and stores them in an accessible format. This one-time conversion effort enables subsequent pattern recognition operations to proceed at high speed without repeated neighbor searches. The dense field structure caches spatial relationships that would otherwise require complex runtime computations, making the initial conversion complexity worthwhile for the sustained performance gains.
Data Source
AI summary
The techniques described herein relate to methods, apparatus, and computer readable media configured to test a pose of a model in three-dimensional data. Three-dimensional data of an object is received, the three-dimensional data comprising a set of data entries. The three-dimensional data is converted to a field comprising a set of cells that each have an associated value, comprising determining, for each cell value, representative data based on one or more data entries from the set of data entries of the three-dimensional data. A pose of the model is tested with the field to determine a score for the pose.