Robust line segment matching across views via ranking the line-point graph

Abstract Line segment matching in two or multiple views is helpful to 3D reconstruction and pattern recognition. To fully utilize the geometry constraint of different features for line segment matching, a novel graph-based algorithm denoted as GLSM (Graph-based Line Segment Matching) is proposed in this paper, which includes: (1) the employment of three geometry types, i.e., homography, epipolar, and trifocal tensor, to constrain line and point candidates across views; (2) the method of unifying different geometry constraints into a line-point association graph for two or multiple views; and (3) a set of procedures for ranking, assigning, and clustering with the line-point association graph. The experimental results indicate that GLSM can obtain sufficient matches with a satisfactory accuracy in both two and multiple views. Moreover, GLSM can be employed with large image datasets. The implementation of GLSM will be available soon at https://skyearth.org/research/ .

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