3D object recognition via simulated particles diffusion

A novel approach for 3D object recognition is presented. This approach is model-based, and assumes either 3D or 21/2 D scene acquisition. Transformation detection is accomplished along with an object identification (six degrees of freedom, three rotational and three translational, are assumed). The diffusion-like simulation recently introduced as a means for characterization of shape is used in the extraction of point features. The point features represent regions on the object's surface that are extreme in curvature (i.e. concavities and convexities). Object matching is carried out by examining the correspondence between the object's set of point features and the model's set of point features, using an alignment strategy. Triangles are constructed between all possible triples of object's point features, and then are aligned to candidate corresponding triangles of the model's point features. 21/2 range images are transformed into a volumetric representation through a parallel projection onto the 3-D space. The resultant volume is suitable for processing by the diffusion-like simulation