Multi-attribute statistics histograms for accurate and robust pairwise registration of range images

A distinctive, robust and efficient 3D local shape descriptor called MaSH is proposed.A novel 3D transformation estimation algorithm named 2SAC-GC is proposed.An accurate and robust range image registration method is presented.The proposed registration method copes well with both high and low resolution data. Registration of range images based on local shape features is widely adopted due to its validated effectiveness and robustness, while most existing local shape descriptors struggle to simultaneously achieve a pleasurable and balanced performance in terms of distinctiveness, robustness and time efficiency. This paper proposes a novel representation of 3D local surfaces, called multi-attribute statistics histograms (MaSH), for automatic registration of range images. MaSH comprises both spatial and geometric information characterizations. The characterization of spatial information is achieved via radial partitions in the 3D local support volume around the keypoint based on a local reference axis (LRA), creating a set of subspaces. While the encoding the shape geometry is performed by calculating statistical histograms of multiple faint correlated geometric attributes (i.e., local depth, normal deviation, and surface variation angle) for each subspace, so as to achieve information complementarity. Then, a robust rigid transformation estimation algorithm named 2-point based sample consensus with global constrain (2SAC-GC) is presented to tackle the problem of calculating an optimal transformation from the correspondence set with outliers. Finally, a coarse-to-fine registration method based on MaSH and 2SAC-GC is proposed for aligning range images. Experiments on both high-resolution (Laser Scanner) and low-resolution (Kinect) datasets report that, our method achieves a registration accuracy of 90.36% and 80.39% on the two datasets, respectively. It also exhibits strong robustness against noise and varying mesh resolutions. Furthermore, feature matching experiments show the over-all superiority of the proposed MaSH descriptor against the state-of-the-arts including the spin image, snapshots, THRIFT, FPFH, RoPS, LFSH and RCS descriptors.