3D Model Alignment Using Edge Points for Symmetric Parts
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Solution Overview
Problem
Existing methods for aligning 3D models with unknown relative orientations, such as those with planar or smooth surfaces and symmetric models with small asymmetric features, face challenges in achieving precise alignment due to ambiguous angles and the need for extensive data labeling and training.
Innovation Solution
A method involving edge point identification, shape descriptor calculation, grouping, and point pair feature (PPF) matching, followed by rotation angle determination and transformation calculation to align 3D models, without requiring extensive training data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If point pair feature (PPF) method is used for aligning 3D models, then alignment can be performed without external frame of reference, but alignment precision deteriorates for planar or smooth surfaces due to ambiguous angles
Solution Approach 1:
The patent applies local quality by differentiating between edge points and non-edge points, and by assigning different weights to points based on their discriminative capability. Edge points with unique geometric characteristics receive higher weights, while points on planar or smooth surfaces are downweighted or excluded. This selective approach maintains the ease of operation while improving alignment precision by focusing on informative features.
Solution Approach 2:
The patent addresses asymmetry by detecting and utilizing asymmetric features in the 3D models. By identifying points with unique geometric configurations (asymmetric edge points) versus symmetric or planar regions, the method can disambiguate rotational orientations that would otherwise be ambiguous on planar surfaces. This asymmetric feature detection resolves the precision issue while preserving the operational simplicity of PPF-based alignment.
2Measurement precision
If boundary point-to-boundary point (B2B) feature is used to handle planar surfaces, then some alignment issues are addressed, but wrong alignment still occurs for symmetric models with small asymmetric features
Solution Approach 1:
The patent extends the local quality principle by introducing weight assignment based on point discriminative capability. Not all edge points are treated equally; instead, points are weighted according to how uniquely they characterize the model geometry. Small asymmetric features with high discriminative power receive higher weights, ensuring they dominate the alignment process even when outnumbered by points from larger symmetric features. This weighted approach resolves the reliability issue while maintaining precision.
Solution Approach 2:
The patent leverages asymmetry by explicitly detecting and emphasizing asymmetric features in the point cloud. By computing geometric descriptors that highlight asymmetric configurations and using these to guide weight assignment, the method ensures that small asymmetric features (which are critical for accurate alignment) are not lost in the noise from larger symmetric features. This asymmetric emphasis corrects wrong alignment while preserving the benefits of B2B feature approach.
3Measurement precision
If learning-based weighting method is used to assign proper weights to points, then alignment precision improves for symmetric models, but the complexity increases due to extensive training data requirements
Solution Approach 1:
The patent applies self-service by enabling the point cloud data itself to provide the weighting information through self-computed geometric descriptors. Instead of requiring external training data or learned models, the method calculates local geometric properties (edge detection, curvature, neighborhood analysis) directly from the input point cloud to determine point weights. This self-service approach eliminates the complexity of training while maintaining the precision benefits of adaptive weighting.
Solution Approach 2:
The patent replaces the mechanical system of deep learning training with a direct geometric computation approach. Instead of using a neural network that requires extensive training data and computational resources, the method uses explicit geometric algorithms to compute edge detection, surface normals, and neighborhood statistics. This substitution maintains the functional capability of adaptive weighting while dramatically reducing complexity and eliminating the need for training infrastructure.
4Device complexity
If all boundary points are considered equally in alignment, then processing is simplified, but alignment accuracy deteriorates because small asymmetric features are not properly weighted
Solution Approach 1:
The patent implements local quality by computing geometric descriptors for each point (edge detection, curvature, surface normal consistency) and using these local properties to determine individual point weights. Points with distinctive geometric characteristics automatically receive higher weights, while generic points receive lower weights. This local differentiation adds minimal processing complexity while dramatically improving alignment precision by ensuring small asymmetric features contribute appropriately to the overall alignment.
Data Source
AI summary
Aligning a source and target model includes calculating a shape descriptor for a plurality of edge points, grouping the edge points by shape descriptor and selecting representative points for each group. Target and source point pair features (PPFs) are calculated between pairs of representative points on the target and source models, where PPFs defines the relative position and orientation of point pairs. Target PPFs are matched with each source PPF and the point pairs associated with the matched PPFs are transformed to align the location and edge direction of a target PPF point with the location and edge direction of a source PPF point. An angle is determined to align the second target PPF point with the second source PPF point, and a modal angle is found among the determined rotation angles. An output transformation is calculated using the transformations associated with the modal angle.