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1. Introduction
Point Cloud Tensor Voting is a commonly used point cloud processing algorithm, which can be used to calculate the normal vector of each point in the point cloud. The basic idea of the algorithm is to use the spatial distribution characteristics of the surrounding point cloud to estimate and vote for the normal vector of each point.
Specific steps are as follows:
For each point, a covariance matrix is built to characterize the spatial distribution of the surrounding point cloud. The calculation of the covariance matrix can use the k-nearest neighbor method, that is, select the k points closest to the point for calculation.
Calculate the eigenvector and eigenvalue according to the covariance matrix, and select the eigenvector corresponding to the smallest eigenvalue as the normal vector of the point.
For each point, vote using the normal vectors of its surrounding points. The voting weight can be calculated by distance weighting or angle weighting.
For the voting results, the average or weighted average method is used to combine them to obtain the final normal vector.
It should be noted that the point cloud tensor voting method is a relatively general point cloud processing method, but when processing different types of point clouds, it may need to be adjusted and optimized to obtain better results. At the same time, for the abnormal points or noise points in the point cloud, certain processing and filtering are also required to avoid the influence on the normal vector calculation results.