Space‐Time Co‐Segmentation of Articulated Point Cloud Sequences
Qing Yuan, Guiqing Li, Kai Xu, Xudong Chen, Hui Fei Huang · Computer Graphics Forum · 2016
Abstract Consistent segmentation is to the center of many applications based on dynamic geometric data. Directly segmenting a raw 3D point cloud sequence is a challenging task due to the low data quality and large inter‐frame variation across the whole sequence. We propose alocal‐to‐globalapproach toco‐segmentpoint cloud sequences of articulated objects into near‐rigid moving parts. Our method starts from a per‐frame point clustering, derived from a robust voting‐based trajectory analysis. The local segments are then progressively propagated to the neighboring frames with a cut propagation operation, and further merged through all frames using a novelspace‐time segment groupingtechnqiue, leading to a globally consistent and compact segmentation of the entire articulated point cloud sequence. Such progressive propagating and merging, in both space and time dimensions, makes our co‐segmentation algorithm especially robust in handling noise, occlusions and pose/view variations that are usually associated with raw scan data.