Point Cloud Registration Using Regression Offset Vectors
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Solution Overview
Problem
The existing point cloud registration techniques are inefficient in processing and unifying point cloud data from different viewing angles, requiring improvements in accuracy and computational efficiency.
Innovation Solution
A computer-implemented method that uses regression for dimension reduction to generate offset vectors and eliminate the need for a 3D grid search, reducing the number of candidate matching points and eliminating the necessity for a 3D neural network for dimension reduction processing, thereby enhancing registration efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional point cloud registration methods are used, then registration accuracy can be achieved, but computational efficiency is poor and processing time is long
Solution Approach 1:
The patent performs preliminary actions by pre-extracting feature points and pre-computing transformation parameters from the first point cloud data before registration. This includes extracting key points, calculating their corresponding points in the second point cloud, and pre-determining transformation matrices, which significantly reduces the computational burden during the actual registration process and improves overall efficiency.
Solution Approach 2:
The patent extracts only the essential feature points and their corresponding transformations from the large point cloud datasets. Instead of processing all points, the method identifies and registers only the key feature points (such as corners, edges, or distinctive surfaces), which dramatically reduces the number of computations required while maintaining registration accuracy.
2Measurement precision
If a 3D grid search method is used to find candidate matching points, then comprehensive search is achieved, but computational complexity increases significantly
Solution Approach 1:
The patent extracts and uses only the necessary transformation information (rotation and translation matrices) derived from feature point correspondences. Instead of performing exhaustive 3D grid searches, the method directly computes matching points using the pre-calculated transformation parameters, significantly reducing computational complexity while maintaining matching accuracy.
Solution Approach 2:
The patent replaces the mechanical 3D grid search approach with a mathematical transformation-based method. Instead of systematically searching through a 3D grid volume to find matching points, the method uses matrix transformations to directly compute the corresponding points, which is computationally much more efficient.
3Productivity
If a 3D neural network is used for dimension reduction processing, then processing capability is enhanced, but system complexity and computational cost increase
Solution Approach 1:
The patent uses simple, lightweight mathematical operations (matrix multiplication and vector transformations) instead of complex 3D neural networks for dimension reduction. These simple computational operations are much cheaper and faster, achieving the same dimension reduction effect with significantly reduced system complexity and computational cost.
Data Source
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AI summary
The present disclosure provides a point cloud data processing method, apparatus, electronic device, computer readable storage medium, and computer program product, which relates to computer vision technology and may be used for autonomous driving. A specific implementation solution is as follows: obtaining a first feature vector of each point in first point cloud data, and determining at least one first key point in the points in the first point cloud data according to the first feature vectors of respective points; according to the at least one first key point and a preset first conversion parameter between second point cloud data and the first point cloud data, obtaining second key points of the second point cloud data corresponding to the first key points in the at least one first key point, as candidate matching points; according to the first point cloud data, the second point cloud data and a preset search radius, determining at least one first neighboring point of the at least one first key point and at least one second neighboring point of the candidate matching point corresponding to the at least one first key point; determining a matching point with which the at least one first key point is registered, according to the at least one first neighboring point of the at least one first key point and the at least one second neighboring point of the candidate matching points.