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.


Published materials

A. Ecins, C. Fermüller, Y. Aloimonos.
Cluttered Scene Segmentation Using the Symmetry Constraint
International Conference on Robotics and Automation (ICRA), May 2016
[Paper] [Poster] [Slides (keynote)]

Sample results


The dataset contains 89 tabletop scenes of varying complexity. It can be downloaded here.