Using 2 point+normal sets for fast registration of point clouds with small overlap

Global 3D point cloud registration has been solved by finding putative matches between the point clouds for establishing alignment hypotheses. A naive approach would try to perform exhaustive search of triplets with a cubic runtime complexity in the number of data points. Super4PCS reduces this complexity to linear by making use of sets of 4 coplanar points. This paper proposes 2-Point-Normal Sets (2PNS), a new global 3D registration approach that advances Super4PCS by using 2 points and their normals for generating alignment hypotheses. The dramatic improvement in the complexity of 2PNS when compared to Super4PCS is demonstrated by the experiments that show speed-ups of two orders of magnitude in noise-free datasets and up to 5.2× in Kinect scans, while improving robustness and alignment accuracy, even in datasets with overlaps as low as 5%.