关键词: 点云体积测量, 点云切片, 多轮廓分割, 多边形内点判定算法, 最近点搜索法

Abstract: When using the slicing method to measure the point cloud volume of irregular objects, the existing Polygon Splitting And Recombination (PSR) method cannot correctly split the nearer contours, resulting in reduced calculation accuracy. Aiming at this problem, a multiple contours segmentation algorithm based on the improved nearest point search method was proposed. Firstly, the segmentation of multiple contours was performed by the single-use principle of local points. Then, the Point Inclusion in Polygon (PIP) algorithm was adopted to judge the inclusion relationship of contours, which confirmed positive and negative of the contour area. Finally, the slice area was multiplied by the thickness and accumulated them to obtain the volume of irregular objects point cloud. The experimental results show that in two sets of public point cloud datasets and one set of chemical electron density isosurface point cloud datasets, the proposed algorithm can achieve high-accuracy boundary segmentation and has certain universality. The average relative error of volume measurement on this basis is 0.0436%, and is lower than 0.0582% of PSR, which achieves high-precision boundary segmentation.

Key words: volume measurement of point cloud, point cloud slicing, multi-contour segmentation, point inclusion in polygon algorithm, nearest point search method