When 3D images are taken at different viewpoints and data fusion is necessary, the rigid transformation between each view needs to be computed, which is called registration or pose estimation. From Horn's work, given three or more pairs of non-coplanar corresponding 3D points, the rigid transformation between the point pairs has a closed form solution. The pose estimation problem becomes the surface point matching problem. This research is about how to compare 3D points.

The goal is to develop a surface representation scheme encoding the local surface geometry, which helps surface matching.

We propose a new efficient surface representation method for the application of surface matching. We generate a feature carrier for the surface point, which is a set of 2D contours that are the projection of geodesic circles onto the tangent plane. The carrier is named a point's fingerprint because its pattern is similar to a human fingerprint and discriminating for each point. Each point's fingerprint carries the information of the normal variation along geodesic circles. Corresponding points on surfaces from different views are found by comparing the fingerprints of the points. This representation scheme includes more local geometry information than some previous works that only use one contour as the feature carrier. It is not histogram-based so that it is able to carry more features to improve comparison accuracy. To speed up the matching, we use a novel candidate point selection method based on the shape irregularity of the projected local geodesic circle.

• Y. Sun and M. A. Abidi, "Surface matching by 3D point's fingerprint," in Proc. IEEE Int'l Conf. on Computer Vision, 2001, vol.II, pp.263-269.