Co-registration of pointclouds by 3D least squares matching

The automatic co-registration of point clouds, representing 3D surfaces, is a relevant problem in 3D modeling. We treat the problem as least squares matching of overlapping surfaces. The surface may have been digitized/sampled point by point using a laser scanner device, a photogrammetric method or other surface measurement techniques. Our proposed method estimates the transformation parameters of one or more 3D search surfaces with respect to a 3D template surface, using the Generalized Gauss-Markoff model, minimizing the sum of squares of the Euclidean distances between the surfaces. This formulation gives the opportunity of matching arbitrarily oriented 3D surfaces. It fully considers 3D geometry. The method derives its mathematical strength from the Least Squares matching concept and offers a high level of flexibility for many kinds of 3D surface correspondence problems. The experiments demonstrate the capabilities of the basic method and the extensions. Examples on the terrain/object modeling, cultural heritage applications, accuracy assessment and change detection are presented.