3D Model Pose Testing via Dense Field Vector Summation
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Solution Overview
Problem
Existing machine vision techniques face inefficiencies when processing three-dimensional data, particularly in searching for patterns, due to the significant time consumed by searching for neighboring points in 3D data, which interrupts parallelization and reduces processing speed.
Innovation Solution
The method involves converting three-dimensional data into a densely-populated field where each cell has associated values, allowing for the determination of representative data and testing model poses by summing dot products of probes with field vectors, thereby avoiding the need to search for neighboring points and improving processing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional machine vision techniques search for neighboring points in 3D data to identify patterns, then pattern recognition accuracy can be maintained, but processing time increases significantly and parallelization is interrupted
Solution Approach 1:
The patent applies preliminary action by pre-computing and storing neighbor relationships in a neighbor map data structure before pattern recognition occurs. This allows the system to quickly retrieve pre-established neighbor information during pattern searching without performing time-consuming searches at runtime, thus maintaining accuracy while reducing processing time
Solution Approach 2:
The patent creates a copied representation of the 3D data in the form of a neighbor map that encodes spatial relationships. Instead of searching the original 3D data structure repeatedly, the system uses this copied neighbor map to quickly determine neighbor relationships, preserving the ability to accurately identify patterns while avoiding repeated search operations
2Measurement precision
If traditional techniques perform sequential searches for neighboring points in 3D data, then accurate pattern identification is achieved, but parallelization efficiency decreases
Solution Approach 1:
The neighbor map is constructed in advance with all neighbor relationships pre-computed and stored. During parallel pattern recognition operations, multiple processors can simultaneously query the neighbor map without interfering with each other, enabling efficient parallelization while maintaining accurate neighbor identification
Solution Approach 2:
The neighbor map serves as an intermediary data structure that mediates between the original 3D data and the pattern recognition algorithms. It provides a centralized, read-optimized interface that multiple parallel processes can access simultaneously without requiring coordinated searches of the original data, thus enabling parallelization while preserving identification accuracy
Data Source
AI summary
The techniques described herein relate to methods, apparatus, and computer readable media configured to test a pose of a three-dimensional model. A three-dimensional model is stored, the three dimensional model comprising a set of probes. Three-dimensional data of an object is received, the three-dimensional data comprising a set of data entries. The three-dimensional data is converted into a set of fields, comprising generating a first field comprising a first set of values, where each value of the first set of values is indicative of a first characteristic of an associated one or more data entries from the set of data entries, and generating a second field comprising a second set of values, where each second value of the second set of values is indicative of a second characteristic of an associated one or more data entries from the set of data entries, wherein the second characteristic is different than the first characteristic. A pose of the three-dimensional model is tested with the set of fields, comprising testing the set of probes to the set of fields, to determine a score for the pose.