3D object retrieval with graph-based collaborative feature learning

Abstract

3D object retrieval has attractive extensive research focus in recent years. Among various schemes, view based 3D object retrieval is regarded as a promising direction. In this paper, we present a novel view-based 3D object retrieval framework, which is deployed over a graph-based collaborative learning scheme to intelligently fuse multiple features. In particular, we introduce a hypergraph based collaborative feature learning scheme to fuse complement descriptors from both the contour and the interior region of 3D object effectively. Then, the view-based 3D object retrieval is done via a greedy bipartite graph matching algorithm, which achieves highly accurate and efficient 3D object matching. With the above bipartite graph matching and feature concatenation, significant performance improvement is achieved in the 3D object retrieval task, on either widely-used benchmark datasets or open competitions like SHREC15 challenge. In both evaluations, the proposed graph-based collaborative feature learning scheme has beaten a serial of existing approaches and state-of-the-art schemes.