Deep Partial Point Cloud Registration via Neural Network Prediction
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Solution Overview
Problem
Existing 3D point cloud registration techniques face challenges in aligning partial point clouds from real-world objects in real-time due to costly combinatorial matching problems and convergence to local optima, especially for small spatial scale registrations.
Innovation Solution
A deep neural network is used to predict the point-wise locations of one point cloud in another's coordinate system without explicit key point matching, determining rotation and translation parameters by encoding local and global features and generating predicted locations based on these features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If key point matching algorithms are used to align two 3D point clouds, then point correspondences can be established, but the process encounters costly combinatorial matching problems and convergence to local optima
Solution Approach 1:
The patent replaces traditional mechanical/key-point-based matching algorithms with a deep neural network that directly predicts 3D transformation parameters (rotation and translation). This substitution eliminates the combinatorial matching process entirely, transforming the problem from discrete point correspondence to continuous parameter prediction, thereby resolving the computational complexity issue while maintaining alignment accuracy
Solution Approach 2:
The invention changes the problem parameters from discrete point matches to continuous transformation parameters (rotation matrix and translation vector). By predicting these parameters directly through a neural network, the system avoids the local optima traps inherent in iterative key-point matching algorithms, achieving both efficiency and accuracy
2Manufacturing precision
If iterative key point matching is performed to converge on alignment parameters, then rotation and translation can be determined, but the process is computationally expensive and slow for real-time applications
Solution Approach 1:
The patent substitutes iterative mechanical optimization processes with a single-pass deep neural network inference. The network is trained offline to learn the mapping from point cloud features to transformation parameters, enabling real-time registration during online operation without iterative convergence, thus achieving both high precision and high speed
Solution Approach 2:
The system performs preliminary training offline where the neural network learns optimal transformation predictions from大量 training data. This preliminary action transfers knowledge to the online phase, enabling fast real-time registration without requiring iterative computation during actual operation, thereby resolving the speed-precision tradeoff
Data Source
AI summary
Devices, systems, and methods are disclosed for partial point cloud registration. In some implementations, a method includes obtaining a first set of three-dimensional (3D) points corresponding to an object in a physical environment, the first set of 3D points having locations in a first coordinate system, obtaining a second set of 3D points corresponding to the object in the physical environment, the second set of 3D points having locations in a second coordinate system, predicting, via a machine learning model, locations of the first set of 3D points in the second coordinate system, and determining transform parameters relating the first set of 3D points and the second set of 3D points based on the predicted location of the first set of 3D points in the second coordinate system.