A Stereo Selecting Method of Multi-view Matching Models Guided Based on Feature Points

Selecting one or more robust matching stereo pairs from redundant overlapping images to reduce the negative influence of incorrect or confusing image information to obtain the most desirable matching results improves multi-view matching ability and quality in multi-view matching techniques. In this paper, a matching quality analysis method for multi-view images is proposed that measures matching robustness based on correctly matched SIFT feature points. Furthermore, based on the method, a feature point guided multi-view image stereo selection matching method is detailed including the basic principles, algorithm, and matching process. Experiments were done on ADS40 multi-view imagery. The results show that the proposed method automatically and efficiently selects images of high matching quality from redundant overlapping images to obtain more correct multi-view matching results. This method is, to some extent, more effective than traditional multi-view matching methods.