TY - GEN
T1 - Learning single-image depth from videos using quality assessment networks
AU - Chen, Weifeng
AU - Qian, Shengyi
AU - Deng, Jia
N1 - Funding Information:
This publication is based upon work partially supported by National Science Foundation under Grant No. 1617767, the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No. OSR-2015-CRG4-2639 and a gift from Google.
Publisher Copyright:
© 2019 IEEE.
PY - 2019/6
Y1 - 2019/6
N2 - Depth estimation from a single image in the wild remains a challenging problem. One main obstacle is the lack of high-quality training data for images in the wild. In this paper we propose a method to automatically generate such data through Structure-from-Motion (SfM) on Internet videos. The core of this method is a Quality Assessment Network that identifies high-quality reconstructions obtained from SfM. Using this method, we collect single-view depth training data from a large number of YouTube videos and construct a new dataset called YouTube3D. Experiments show that YouTube3D is useful in training depth estimation networks and advances the state of the art of single-view depTH ESTIMAtion in the wild.
AB - Depth estimation from a single image in the wild remains a challenging problem. One main obstacle is the lack of high-quality training data for images in the wild. In this paper we propose a method to automatically generate such data through Structure-from-Motion (SfM) on Internet videos. The core of this method is a Quality Assessment Network that identifies high-quality reconstructions obtained from SfM. Using this method, we collect single-view depth training data from a large number of YouTube videos and construct a new dataset called YouTube3D. Experiments show that YouTube3D is useful in training depth estimation networks and advances the state of the art of single-view depTH ESTIMAtion in the wild.
KW - 3D from Single Image
KW - Datasets and Evaluation
UR - http://www.scopus.com/inward/record.url?scp=85078792988&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85078792988&partnerID=8YFLogxK
U2 - 10.1109/CVPR.2019.00575
DO - 10.1109/CVPR.2019.00575
M3 - Conference contribution
AN - SCOPUS:85078792988
T3 - Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
SP - 5597
EP - 5606
BT - Proceedings - 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019
PB - IEEE Computer Society
T2 - 32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019
Y2 - 16 June 2019 through 20 June 2019
ER -