Attention-driven segmentation of cluttered 3D scenes

Vision is an essential part in robotic systems, where attention plays an important role to cope with the complexity of the real world. Attention mechanisms have been proposed in the past to guide search and also segmentation of objects. Building on recent advances in affordable 3D sensing we first attend to objects using a novel saliency map, based on color and depth information. We then segment attended objects using an edge map that uses color, depth and curvature within a probabilistic framework. We present an improvement over existing methods regarding the quality of attention points, in terms of their location within the object and the number of attended objects. Together the proposed attention points and probabilistic edges lead to a significant improvement of segmentation results compared to existing methods of active segmentation¹.