Recognition of 3D free-form objects

We address the problem of recognizing 3D rigid freeform objects using dense range data when the objects can be imaged from arbitrary viewpoints and the objects vary in shape and complexity. We propose a multi-level matching strategy that employs shape spectral analysis and features derived from the COSMOS representations of free-form objects for fast and efficient recognition. We demonstrate that with a large model database of object views, a small set of ranked candidate matches can be selected quickly using shape spectrum based matching for further verification. We propose a graph-based matching scheme for view hypothesis verification using COSMOS representations of object views to establish the correct identity and the pose of the sensed object. Preliminary experimental results on a database containing views of ten different objects are shown to demonstrate the effectiveness of the COSMOS-based 3D object recognition system.