TY - GEN
T1 - Dense disparity maps from sparse disparity measurements
AU - Hawe, Simon
AU - Kleinsteuber, Martin
AU - Diepold, Klaus
PY - 2011
Y1 - 2011
N2 - In this work we propose a method for estimating disparity maps from very few measurements. Based on the theory of Compressive Sensing, our algorithm accurately reconstructs disparity maps only using about 5% of the entire map. We propose a conjugate subgradient method for the arising optimization problem that is applicable to large scale systems and recovers the disparity map efficiently. Experiments are provided that show the effectiveness of the proposed approach and robust behavior under noisy conditions.
AB - In this work we propose a method for estimating disparity maps from very few measurements. Based on the theory of Compressive Sensing, our algorithm accurately reconstructs disparity maps only using about 5% of the entire map. We propose a conjugate subgradient method for the arising optimization problem that is applicable to large scale systems and recovers the disparity map efficiently. Experiments are provided that show the effectiveness of the proposed approach and robust behavior under noisy conditions.
UR - http://www.scopus.com/inward/record.url?scp=84856657292&partnerID=8YFLogxK
U2 - 10.1109/ICCV.2011.6126488
DO - 10.1109/ICCV.2011.6126488
M3 - Conference contribution
AN - SCOPUS:84856657292
SN - 9781457711015
T3 - Proceedings of the IEEE International Conference on Computer Vision
SP - 2126
EP - 2133
BT - 2011 International Conference on Computer Vision, ICCV 2011
T2 - 2011 IEEE International Conference on Computer Vision, ICCV 2011
Y2 - 6 November 2011 through 13 November 2011
ER -