Automatic registration of 3D datasets using Gaussian fields

In this paper we introduce a new 3D automatic registration method based on Gaussian fields and energy minimization. The method defines a simple C^∞ energy function, which is convex in a large neighborhood of the alignment parameters; allowing for the use of powerful standard optimization techniques. We show that the size of the region of convergence can be significantly extended reducing the need for close initialization and overcoming local convergence problems of the standard iterative closest point (ICP) algorithms. Furthermore, the Gaussian criterion can be evaluated with linear computational complexity using fast Gauss transform methods, allowing for an efficient implementation of the registration algorithm. Experimental analysis of the technique using real world datasets shows the usefulness as well as the limits of the approach.