Recognition of 3D free-form objects
We address the problem of recognizing 3D rigid free-form 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 often different objects are shown to demonstrate the effectiveness of the COSMOS-based 3D object recognition system.