A fast spin images matching method for 3D object recognition

Spin image has been applied to 3D object recognition system successfully because of its advantages of rotation, translation and view invariant. However, this method is very time consuming, owning to its high-dimensional characteristics and its complicated matching procedure. To reduce the recognition time, in this paper we propose a coarse-to-fine matching strategy for spin images. There are two steps to follow. Firstly, a low dimensional feature is introduced for a given point. The feature contain two components, its first component is the perpendicular distance from the centroid of the given point’s neighbor region to the tangential plane of the given point, its second component is the maximum distance between the projection point of the centroid on the tangential plane and projection points of the neighbor region on the tangential plane. Secondly when comparing a point from a target with a point from a model, their low features are matched first, only if they satisfy the low feature constrains, can they be selected as a candidate point pair and their spin images are further matched by similarity measurement. When all the target points and all the model points finish above matching process, those candidate point pairs with high spin image similarity are selected as corresponding point pairs, and the target can be recognized as the model with the most amount of corresponding point pairs. Experiment based on Stanford 3D models is conducted, and the comparison of experiment results of our method with the standard spin image shows that the propose method is more efficient while still maintain the standard spin image’s advantages.