Shape (Self-)Similarity and Dissimilarity Rating for Segmentation and Matching

Similarities and dissimilarities can be found in many natural as well as man-made structures and are an important source of information, e.g., for isolating defects or pathological regions, and for finding unique points and regions of interest on surfaces. This paper introduces a new approach for computing similarity information that can be used, e.g., for surface segmentation or to guide a subsequent registration. The method is based on a probabilistic matching algorithm generating possible partial matches between shapes. For each point of a source surface we analyse the distribution of similar regions on a reference surface. In this way, we obtain a point-wise similarity rating between the source and reference shape. In our experimental evaluation we demonstrate the usability and show some excellent results on several 3D objects, like industrial CAD data sets, bone fractures, and potteries.