TY - GEN
T1 - Wide baseline stereo matching with convex bounded distortion constraints
AU - Galun, Meirav
AU - Amir, Tal
AU - Hassner, Tal
AU - Basri, Ronen
AU - Lipman, Yaron
N1 - Publisher Copyright: © 2015 IEEE.
PY - 2015/2/17
Y1 - 2015/2/17
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84973866039&partnerID=8YFLogxK
U2 - https://doi.org/10.1109/ICCV.2015.257
DO - https://doi.org/10.1109/ICCV.2015.257
M3 - منشور من مؤتمر
SN - 9781467383905
T3 - IEEE International Conference on Computer Vision
SP - 2228
EP - 2236
BT - 2015 IEEE International Conference on Computer Vision
T2 - IEEE International Conference on Computer Vision
Y2 - 11 December 2015 through 18 December 2015
ER -