Alignment of 3D shape data by hashing sets of feature points

This paper presents a method to automatically align a pose of 3D shape data to fit another shape data taken from different viewpoints. One of the difficult issues is to handle shape data which have surface information in different sides due to the difference in viewpoints, and to deal with objects in different scale. We detect local feature points on the two shape data, make potentially corresponding pairs of three feature points, calculate transformation parameters to align the three points, and get optimal alignment parameters by the voting of parameters obtained from the pairs of three points. We used hash table to avoid combinatorial explosion in making the pairs, and used geometric invariants for its key which are calculated from the positions of the points to keep the scale invariance. The method was evaluated with some public data and a set of laser-scanned data, and proved to be effective in alignment of shape data in different angles or scales.