Seeing Behind the Scene: Using Symmetry to Reason About Objects in Cluttered Environments

Abstract

Symmetry is a common property shared by the majority of man-made objects. This paper presents a novel bottom-up approach for segmenting symmetric objects and recovering their symmetries from 3D pointclouds of natural scenes. Candidate rotational and reflectional symmetries are detected by fitting symmetry axes/planes to the geometry of the smooth surfaces extracted from the scene. Individual symmetries are used as constraints for the foreground segmentation problem that uses symmetry as a global grouping principle. Evaluation on a challenging dataset shows that our approach can reliably segment objects and extract their symmetries from incomplete 3D reconstructions of highly cluttered scenes, outperforming state-of-the-art methods by a wide margin.