3D Point Cloud Alignment for Articulatable Part Identification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods struggle to accurately identify and isolate articulatable parts of physical objects using 3D point clouds without requiring complex CAD models or expert knowledge, especially when dealing with noisy reconstructions from commodity 3D sensors.
Innovation Solution
A computer-implemented method and system that aligns initial and post-articulation 3D point clouds, identifies nearest neighbors, and eliminates noise points to isolate articulatable parts, using a two-step alignment process involving coarse and fine alignment, and efficient nearest neighbor search techniques, allowing for the generation of approximate CAD models without expert intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex CAD models or expert knowledge are used to identify articulatable parts, then identification accuracy is improved, but device complexity and ease of operation deteriorate
Solution Approach 1:
The patent creates a digital copy (3D point cloud) of the physical object using commodity 3D sensors, eliminating the need for complex physical CAD models. The point cloud serves as a simplified digital representation that can be processed algorithmically to identify articulatable parts without requiring expert knowledge or complex modeling tools.
Solution Approach 2:
The patent replaces the manual expert analysis mechanism with an automated computational mechanism. By using algorithms to compare point clouds from different articulation states and identify differing points, the system substitutes human expert knowledge with automated image processing techniques, improving both accuracy and ease of operation.
2Measurement precision
If complex CAD models or expert knowledge are used to identify articulatable parts, then identification accuracy is improved, but ease of operation deteriorates
Solution Approach 1:
The system uses 3D point cloud copies captured by commodity sensors to represent the physical object, allowing non-experts to work with simple digital data instead of complex CAD models. This copying approach makes the system accessible to users without specialized training while maintaining identification accuracy through automated comparison algorithms.
Solution Approach 2:
The system enables non-expert users to perform articulatable part identification independently through automated processing. The algorithm automatically compares point clouds, identifies differing points, and isolates articulatable parts without requiring user expertise, making the system self-sufficient and easy to operate for anyone with basic computer skills.
3Ease of manufacture
If commodity 3D sensors are used to capture point clouds, then ease of manufacture and accessibility are improved, but measurement precision deteriorates due to noisy reconstructions
Solution Approach 1:
The patent extracts only the relevant information from noisy point cloud data by comparing points between different articulation states. By focusing on differing points between the initial and articulated point clouds, the system isolates the articulatable parts while filtering out noise and irrelevant variations, maintaining precision despite using commodity sensors.
Solution Approach 2:
The patent introduces an intermediary processing step (point cloud comparison and nearest neighbor matching) between data capture and identification. This intermediary algorithmic layer corrects for sensor noise by finding corresponding points across multiple scans and articulation states, effectively filtering out measurement errors while preserving genuine articulation movements.
4Measurement precision
If multiple point clouds are aligned and processed to identify articulatable parts, then identification accuracy is improved, but processing time increases
Solution Approach 1:
The patent segments the point cloud processing into distinct stages: coarse alignment to establish rough correspondence, fine alignment to refine point matching, and differential analysis to identify articulatable parts. This segmentation allows each stage to be optimized independently, improving overall accuracy while managing processing time through efficient algorithm design at each step.
Data Source
AI summary
An apparatus comprises an input interface configured to receive a first 3D point cloud associated with a physical object prior to articulation of an articulatable part, and a second 3D point cloud after articulation of the articulatable part. A processor is operably coupled to the input interface, an output interface, and memory. Program code, when executed by the processor, causes the processor to align the first and second point clouds, find nearest neighbors of points in the first point cloud to points in the second point cloud, eliminate the nearest neighbors of points in the second point cloud such that remaining points in the second point cloud comprise points associated with the articulatable part and points associated with noise, generate an output comprising at least the remaining points of the second point cloud associated with the articulatable part without the noise points, and communicate the output to the output interface.