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8 March 2018 Geometry and coherence based feature matching for structure from motion
Kai Wei, Kun Sun, Wenbing Tao
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Proceedings Volume 10609, MIPPR 2017: Pattern Recognition and Computer Vision; 1060917 (2018) https://doi.org/10.1117/12.2285579
Event: Tenth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2017), 2017, Xiangyang, China
Abstract
We present a fast feature matching approach based on coherence and geometry constraints. Our method first estimates the epipolar geometry between the images with a small number of feature points, then uses the epipolar geometry constraint and the coherence among the matches to guide the matching of the remaining features. For the rest of the feature points, we firstly reduce the scope of the candidate matching points according to the epipolar geometry constraint. After that, we use the coherence constraint, which requires the matching points of neighboring feature points to be neighbors, to further reduce the number of the candidate matching points. Such a strategy can effectively reduce the matching time and retain more correct matches which are filtered by David Lowe’s ratio test. Finally, we remove the mismatches roughly with the coherence among the matches. We validate the effectiveness of our method through matching and SfM results on various of public datasets.
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Kai Wei, Kun Sun, and Wenbing Tao "Geometry and coherence based feature matching for structure from motion", Proc. SPIE 10609, MIPPR 2017: Pattern Recognition and Computer Vision, 1060917 (8 March 2018); https://doi.org/10.1117/12.2285579
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KEYWORDS
Coherence (optics)
3D modeling
3D image reconstruction
Reconstruction algorithms
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