An approach to boundary detection for 3D point clouds based on DBSCAN clustering

This paper introduces a new DBSCAN-based method for boundary detection and plane segmentation for 3D point clouds. The proposed method is based on candidate samples selection in 3D space and plane validity detection via revising the classical DBSCAN clustering algorithm to obtain a valid fitting plane. Technically, a coplanar threshold is designed as an additional clustering condition to group 3D points whose distances to the fitting plane satisfy the constraint of the threshold as one cluster. The threshold value is automatically adjusted to fit the local distribution of samples in the input dataset, which is free of parameter tuning. Planar objects can be detected by the proposed method since a cluster contains only data points belonging to one plane, and the boundaries among different planes can be correctly detected. Experimental evaluations are performed on both synthetic and real point cloud datasets. Results show that the proposed approach is effective for planar segmentation and high-quality segmentation of intersection boundaries.