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