Hierarchical Object Discovery and Dense Modelling From Motion Cues in RGB-D Video

In this paper, we propose a novel method for object discovery and dense modelling in RGB-D image sequences using motion cues. We develop our method as a building block for active object perception, such that robots can learn about the environment through perceiving the effects of actions. Our approach simultaneously segments rigid-body motion within key views, and discovers objects and hierarchical relations between object parts. The poses of the key views are optimized in a graph of spatial relations to recover the rigid-body motion trajectories of the camera with respect to the objects. In experiments, we demonstrate that our approach finds moving objects, aligns partial views on the objects, and retrieves hierarchical relations between the objects.