Iterative closest point for accurate plane detection in unorganized point clouds

Abstract

Plane detection is an important step in the reconstruction of 3D models of buildings from point clouds. The results of plane detection methods based on the region growing approach mainly depend on the choice of seed points. In this study, we introduce a novel region growing-based method for plane detection in unorganized point clouds. Our method uses the Iterative Closest Point (ICP) algorithm to extract reliable seeds. To enhance the performance and the quality of the results, we used voxel grids representation of the point clouds in the growing process. The classification of the candidate planes is improved by using the number of voxel cells covering accumulated segments. The method is deterministic, runs in O(nlog(n)), and does not take into account the orientation of the point clouds. The results of plane detection using the proposed method on a benchmark consisting of 9 point clouds of buildings show a better precision of extracted planes compared to those obtained with 3-D KHT and PCL-RANSAC. Although slower than 3-D KHT, our method requires an execution time (3 x times) shorter than PCL-RANSAC.