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.

Although modern object segmentation algorithms can deal with isolated objects in simple scenes, segmenting non-convex objects in cluttered environments remains a challenging task. We introduce a novel approach for segmenting unknown objects in partial 3D pointclouds that utilizes the powerful concept of symmetry. First, 3D bilateral symmetries in the scene are detected efficiently by extracting and matching surface normal edge curves in the pointcloud. Symmetry hypotheses are then used to initialize a segmentation process that finds points of the scene that are consistent with each of the detected symmetries. We evaluate our approach on a dataset of 3D pointcloud scans of tabletop scenes. We demonstrate that the use of the symmetry constraint enables our approach to correctly segment objects in challenging configurations and to outperform current state-of-the-art approaches.

Over the last decades several approaches were introduced to deal with cast shadows in background subtraction applications. However, very few algorithms exist that address the same problem for still images. In this paper we propose a figure ground segmentation algorithm to segment objects in still images affected by shadows. Instead of modeling the shadow directly in the segmentation process our approach works actively by first segmenting an object and then testing the resulting boundary for the presence of shadows and resegmenting again with modified segmentation parameters. In order to get better shadow boundary detection results we introduce a novel image preprocessing technique based on the notion of the image density map. This map improves the illumination invariance of classical filter-bank based texture description methods. We demonstrate that this texture feature improves shadow detection results. The resulting segmentation algorithm achieves good results on a new figure ground segmentation dataset with challenging illumination conditions.