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We present an algorithm to segment an unstructured table top scene. Operating on the depth image of a Kinect camera, the algorithm robustly separates objects of previously unknown shape in cluttered scenes of stacked and partially occluded objects. The model-free algorithm finds smooth surface patches which are subsequently combined to form object hypotheses. We evaluate the algorithm regarding its robustness and real-time capabilities and discuss its advantages compared to existing approaches as well as its weak spots to be addressed in future work. We also report on an autonomous grasping experiment with the Shadow Robot Hand which employs the estimated shape and pose of segmented objects.