Model-based recognition using 3D structure from motion

摘要

The effectiveness of shape information alone, without size, for recognizing stored 3D models is considered. The geometric constraint filtering method of Grimson and Lozano-Pérez is used to curb the potentially combinatorial expansion of the model search space. Results, typical of those from several models experimented with, are given for the task of recognizing a plug from uncertain surface normal data. They show that, at least for an ‘interesting’ view, shape data is highly effective, even when the sensed surface normals are uncertain in direction to, say, ± 10°. The loss of size information does, however, result in a drop in search efficiency, but this appears relatively small: u factor of ≈ 5 for the example of a plug at the 10° error mark.