MODEL INDEXING AND OBJECT RECOGNITION USING 3D VIEWPOINT INVARIANCE

Object recognition and location of 3D objects from range data are desc ribed, based on invariant representations of characteristic views of s ingle objects. Depth data is processed to remove outliers, then smooth ed and segmented into surface patches, fitted by polynomial equations. Mathematical invariants are derived from groups of these surface patc hes and used for indexing a model database stored in the form of a kd tree. The pose of the known object may then be determined from the Euc lidean centre and orientation of the higher order surfaces formed from the feature groups. The approach is complete, scalable and extendable . The robustness of the method is evaluated in terms of the stability and discriminatory power of the representation, and the accuracy of th e determined pose. (C) 1997 Pattern Recognition Society. Published by Elsevier Science Ltd.