RECOGNIZING 3D OBJECTS IN CLUTTERED SCENES USING PROJECTION IMAGES

This paper presents a novel descriptor for recognizing objects in highly occluded and cluttered 2.5D scenes produced by range scans. This new compact regional shape descriptor, called "projection images", is designed to be robust against noise, partial occlusion and clutter. Projection images are formed by "projections" of points onto the plane centered at the basis point which is perpendicular to the viewing axis. Multiple experiments were performed on a dataset of 50 range scans, each one comprised of multiple objects, which proved that the proposed method is robust and efficient to a satisfactory degree of occlusion and clutter, while it compared favorably against descriptors previously introduced in the literature.