Realtime 3D segmentation for human-robot interaction

We present a real-time algorithm that segments unstructured and highly cluttered scenes. The algorithm robustly separates objects of unknown shape in congested scenes of stacked and occluded objects. The model-free approach finds smooth surface patches, using a depth image from a Kinect camera, which are subsequently combined to form highly probable object hypotheses. Coplanarity and curvature matching is used to recombine surfaces separated by occlusion. The real-time capabilities are proven and the quality of the algorithm is evaluated on a benchmark database. Advantages compared to existing approaches as well as weaknesses are discussed.