Efficient 3D correspondence grouping by two-stage filtering

Abstract

This paper presents an efficient 3D correspondence grouping algorithm for finding inliers from an initial set of feature matches. The novelty of our approach lies in the use of a combination of pair-wise and triple-wise geometric constraints to filter the outliers from the initial correspondence. The triple-wise geometric constraint is built by considering three pairs of corresponding points simultaneously. A global reference point generated according to the model shape can be mapped to the scene shape thereby form a derived point by the triple-wise geometric constraint. Then, all the initial correspondence can be filtered once via the global reference point and the derived point by using some simple and low-level geometric constraints. Afterwards, the remaining correspondences will be further filtered by means of the pair-wise geometric consistency algorithm. Finally, more accurate matching results can be obtained. The experimental results show the superior performance of our approach with respect to the noise, point density variation and partial overlap. Our algorithm strikes a good balance between accuracy and speed.