Surface Scan Comparison Using Reduced Spin-Image Pose Estimation
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Solution Overview
Problem
Existing pose estimation systems, particularly those using the spin-image algorithm, are computationally complex and time-consuming, making them impractical for real-time applications, and often require immobilization of objects or consistent scanner positioning, which is cumbersome and costly.
Innovation Solution
A surface data acquisition, storage, and assessment system that utilizes a reduced representation of spin-images (from 256 numbers to less than 10) and employs a similarity measure to quickly estimate poses, allowing for real-time comparisons and eliminating the need for object immobilization or consistent scanner positioning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the spin-image algorithm is used for pose estimation, then measurement precision is improved, but computing time increases and productivity decreases
Solution Approach 1:
The patent extracts only the essential features from spin-images that are necessary for pose estimation, reducing the data from 256 numbers to less than 10 numbers per spin-image. This extraction of critical information maintains measurement precision while dramatically reducing computational complexity and enabling real-time processing.
Solution Approach 2:
The patent changes the parameter representation from 256-dimensional spin-image data to a reduced set of less than 10 parameters per spin-image. This parameter reduction transforms the computational burden while preserving the essential geometric information needed for accurate pose estimation, thereby improving productivity without sacrificing measurement precision.
2Measurement precision
If traditional pose estimation systems are used, then measurement precision is improved, but device complexity and cost increase due to immobilization requirements
Solution Approach 1:
The patent enables the scanning system to automatically adapt to different object positions and orientations without requiring external immobilization devices. The reduced spin-image representation and similarity measure algorithm allow the system to self-correct for positioning variations, eliminating the need for complex immobilization mechanisms while maintaining pose detection accuracy.
Solution Approach 2:
The patent introduces dynamic adaptability to the pose estimation system by using similarity measures that can handle variations in object positioning and orientation. This dynamic approach allows the system to accommodate moving or repositioned objects without requiring fixed immobilization setups, thereby reducing device complexity while preserving measurement precision.
3Measurement precision
If traditional pose estimation systems are used, then measurement precision is improved, but loss of time increases due to processing complexity
Solution Approach 1:
The patent extracts only the most critical features from the spin-image data, reducing each spin-image representation from 256 numbers to less than 10 numbers. This extraction process maintains the essential geometric information needed for accurate pose estimation while dramatically reducing the computational time required for processing, thereby eliminating the trade-off between precision and speed.
Solution Approach 2:
The patent implements a simplified similarity measure that allows for rapid comparison of reduced spin-images. By skipping the computationally intensive operations of traditional pose estimation and using only the essential reduced parameters, the system achieves both high measurement precision and fast processing speeds, eliminating time loss.
Data Source
AI summary
A scanning system having a display that operates to display a first scanned image selected by a user and a second scanned image selected by a user and a comparison image showing differences between the first scanned image and the second scanned image.