Hierarchical co-segmentation of 3D point clouds for indoor scene

Abstract

Segmentation of point clouds has been studied under a variety of scenarios. However, the segmentation of scanned point clouds for a clustered indoor scene remains significantly challenging due to noisy and incomplete data, as well as scene complexity. Based on the observation that objects in an indoor scene vary largely in scale but are typically supported by planes, we propose a co-segmentation approach. This technique utilizes the mutual agency between the point clouds captured at different times after the objects' poses change due to human actions. Hence, we hierarchically segment scenes from different times into patches and generate tree structures to store their relations. By iteratively clustering patches and co-analyzing them based on the relations between patches, we modify the tree structures and generate our results. To test the robustness of our method, we evaluate it on imperfectly scanned point clouds from a childroom, a bedroom, and two offices scenes.