In this paper, we present a new algorithm for the alignment of two 3D scans. The approach uses a region-based matching technique. We make no assumptions about the initial positions of the scans. Regions are described by a probability density function (pdf) computed from low dimensional surface descriptors (curvature or normal cone). The algorithm allows registering directly raw noisy data, possibly with the presence of outliers, without any pre-processing, such as filtering, denoising, or reconstruction. Region correspondence is found using similarity function based on the comparison of regions pdf and under geometry constraints. Results on raw scan data sets are presented to illustrate and evaluate the algorithm.