Robust 3-D Plane Segmentation From Airborne Point Clouds Based on Quasi-A-Contrario Theory

Three-dimensional (3-D) plane segmentation has been and continues to be a challenge in 3-D point cloud processing. The current methods typically focus on the planar subsets separation but ignore the requirement of the precise plane fitting. We propose a quasi-a-contrario theory-based plane segmentation algorithm, which is capable of dealing with point clouds of severe noise level, low density, and high complexity robustly. The main proposition is that the final plane can be composed of basic planar subsets with high planar accuracy. We cast planar subset extraction from the point set as a geometric rigidity measuring problem. The meaningfulness of the planar subset is estimated by the number of false alarms (NFA), which can be used to eliminate false-positive effectively. Experiments were conducted to analyze both the planar subset extraction and the 3-D plane segmentation. The results show that the proposed algorithms perform well in terms of accuracy and robustness compared with state-of-art methods. Experimental datasets, results, and executable program of the proposed algorithm are available at https://skyearth.org/publication/project/QTPS.