Wide baseline stereo matching with convex bounded-distortion constraints
This addresses the challenging problem of wide baseline stereo matching for computer vision applications, representing an incremental improvement by focusing on correspondence after epipolar constraints are established.
The paper tackles the problem of finding correspondences in wide baseline stereo matching once approximate epipolar constraints are given, introducing a method that integrates a deformation model to find the largest number of corresponding points under bounded distortion, resulting in significantly more accurate maps than existing approaches.
Finding correspondences in wide baseline setups is a challenging problem. Existing approaches have focused largely on developing better feature descriptors for correspondence and on accurate recovery of epipolar line constraints. This paper focuses on the challenging problem of finding correspondences once approximate epipolar constraints are given. We introduce a novel method that integrates a deformation model. Specifically, we formulate the problem as finding the largest number of corresponding points related by a bounded distortion map that obeys the given epipolar constraints. We show that, while the set of bounded distortion maps is not convex, the subset of maps that obey the epipolar line constraints is convex, allowing us to introduce an efficient algorithm for matching. We further utilize a robust cost function for matching and employ majorization-minimization for its optimization. Our experiments indicate that our method finds significantly more accurate maps than existing approaches.