Robust Surface Area Measurement of Unorganized Point Clouds Based on Multiscale Supervoxel Segmentation

Abstract

Most of the existing surface area measurement methods suffer from poor efficiency, low precision, and high computational cost, especially for inaccessible, large-scale, rough, and curved surfaces. In this article, we propose a method to directly measure the surface areas of unorganized point clouds applicable to various scenes. First, an adaptive supervoxel segmentation algorithm is adopted to divide the input point cloud into a collection of facets with multiple scales. For each facet, all points belonging to it are projected onto its corresponding accurately fit plane. Second, for each projected facet, rigid transform is performed so that its normal vector is parallel to the Z-axis. For each 2-D facet point cloud, the x-coordinates and-coordinates are utilized to abstract its boundary points. Third, the boundary points are sorted in clockwise order so that every two adjacent points and the center point determine a triangle. Next, an improved interpolation method is adopted to interpolate the sparse edge points. The surface area calculation results of different scales can be obtained by counting the sum of the triangular area inside each facet. Finally, the optimum value is determined from these results. The proposed method is tested on various types of point clouds acquired in different ways. Comprehensive experiments demonstrate that the proposed method is efficient and effective and is capable of obtaining good performances in both simple regular planes and complex surfaces. In particular, compared with traditional reconstruction-based methods, the proposed method significantly outperforms when dealing with large-scale and complex scenes.