Model indexing and object recognition using 3D viewpoint invariance

Abstract

Object recognition and location of 3D objects from range data are described, based on invariant representations of characteristic views of single objects. Depth data is processed to remove outliers, then smoothed and segmented into surface patches, fitted by polynomial equations. Mathematical invariants are derived from groups of these surface patches 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 Euclidean 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 the determined pose.