3D object recognition by indexing structural invariants from multiple views

The authors present a method for 3-D object recognition from 2-D image sequences. The system uses feature points tracked over three or more views to compute structural invariants, which serve as 3-D shape representations. Object recognition is performed by using these Euclidean invariants as indices into a high-dimensional shape table. The use of indexing eliminates any need for matching models to images. In addition, the representation of 3-D objects is extracted from 2-D views, eliminating the cumbersome burden of having to obtain 3-D models. The proposed scheme was implemented using a mixed database of real and simulated objects. Experiments are outlined that show good recognition results on real objects and simulated objects corrupted with noise