Sparse3D: A new global model for matching sparse RGB-D dataset with small inter-frame overlap. (September 2018) - Le, Canyu; Li, Xin
Abstract: We present a novel 3D global matching algorithm, Sparse3D, to handle the challenging reconstruction of RGB-D datasets whose inter-frame overlap is small due to insufficient temporal sampling or fast camera movement. To support a more reliable reconstruction, two major technical components are proposed: (1) pairwise alignment using a set of complementary features, and (2) a novel global model for alignment pruning and pose optimization. We examine the effectiveness of our algorithm on multiple benchmark datasets under various inter-frame overlap, and demonstrate it better reliability over existing RGB-D reconstruction algorithms. Graphical abstract: Highlights: A new robust algorithm for reconstruction from sparse RGB-D data with small inter-frame overlap. A novel global model to prune and optimize local pairwise alignment. A reconstruction model for unordered RGB-D data.