TY - GEN
T1 - Combining semantic scene priors and haze removal for single image depth estimation
AU - Wang, Ke
AU - Dunn, Enrique
AU - Tighe, Joseph
AU - Frahm, Jan Michael
PY - 2014
Y1 - 2014
N2 - We consider the problem of estimating the relative depth of a scene from a monocular image. The dark channel prior, used as a statistical observation of haze free images, has been previously leveraged for haze removal and relative depth estimation tasks. However, as a local measure, it fails to account for higher order semantic relationship among scene elements. We propose a dual channel prior used for identifying pixels that are unlikely to comply with the dark channel assumption, leading to erroneous depth estimates. We further leverage semantic segmentation information and patch match label propagation to enforce semantically consistent geometric priors. Experiments illustrate the quantitative and qualitative advantages of our approach when compared to state of the art methods.
AB - We consider the problem of estimating the relative depth of a scene from a monocular image. The dark channel prior, used as a statistical observation of haze free images, has been previously leveraged for haze removal and relative depth estimation tasks. However, as a local measure, it fails to account for higher order semantic relationship among scene elements. We propose a dual channel prior used for identifying pixels that are unlikely to comply with the dark channel assumption, leading to erroneous depth estimates. We further leverage semantic segmentation information and patch match label propagation to enforce semantically consistent geometric priors. Experiments illustrate the quantitative and qualitative advantages of our approach when compared to state of the art methods.
UR - http://www.scopus.com/inward/record.url?scp=84904636661&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84904636661&partnerID=8YFLogxK
U2 - 10.1109/WACV.2014.6836021
DO - 10.1109/WACV.2014.6836021
M3 - Conference contribution
AN - SCOPUS:84904636661
SN - 9781479949854
T3 - 2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014
SP - 800
EP - 807
BT - 2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014
T2 - 2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014
Y2 - 24 March 2014 through 26 March 2014
ER -