Attention-driven object detection and segmentation of cluttered table scenes using 2.5D symmetry

The task of searching and grasping objects in cluttered scenes, typical of robotic applications in domestic environments requires fast object detection and segmentation. Attentional mechanisms provide a means to detect and prioritize processing of objects of interest. In this work, we combine a saliency operator based on symmetry with a segmentation method based on clustering locally planar surface patches, both operating on 2.5D point clouds (RGB-D images) as input data to yield a novel approach to table-top scene segmentation. Evaluation on indoor table-top scenes containing man-made objects clustered in piles and dumped in a box show that our approach to selection of attention points significantly improves performance of state-of-the-art attention-based segmentation methods.