TY - GEN
T1 - Stereo computation for a single mixture image
AU - Zhong, Yiran
AU - Dai, Yuchao
AU - Li, Hongdong
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2018.
PY - 2018
Y1 - 2018
N2 - This paper proposes an original problem of stereo computation from a single mixture image – a challenging problem that had not been researched before. The goal is to separate (i.e., unmix) a single mixture image into two constitute image layers, such that the two layers form a left-right stereo image pair, from which a valid disparity map can be recovered. This is a severely illposed problem, from one input image one effectively aims to recover three (i.e., left image, right image and a disparity map). In this work we give a novel deep-learning based solution, by jointly solving the two subtasks of image layer separation as well as stereo matching. Training our deep net is a simple task, as it does not need to have disparity maps. Extensive experiments demonstrate the efficacy of our method.
AB - This paper proposes an original problem of stereo computation from a single mixture image – a challenging problem that had not been researched before. The goal is to separate (i.e., unmix) a single mixture image into two constitute image layers, such that the two layers form a left-right stereo image pair, from which a valid disparity map can be recovered. This is a severely illposed problem, from one input image one effectively aims to recover three (i.e., left image, right image and a disparity map). In this work we give a novel deep-learning based solution, by jointly solving the two subtasks of image layer separation as well as stereo matching. Training our deep net is a simple task, as it does not need to have disparity maps. Extensive experiments demonstrate the efficacy of our method.
KW - Anaglyph
KW - Double vision
KW - Image separation
KW - Monocular depth estimation
KW - Stereo computation
UR - http://www.scopus.com/inward/record.url?scp=85055127360&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-01240-3_27
DO - 10.1007/978-3-030-01240-3_27
M3 - Conference contribution
SN - 9783030012397
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 441
EP - 456
BT - Computer Vision – ECCV 2018 - 15th European Conference, 2018, Proceedings
A2 - Hebert, Martial
A2 - Ferrari, Vittorio
A2 - Sminchisescu, Cristian
A2 - Weiss, Yair
PB - Springer Verlag
T2 - 15th European Conference on Computer Vision, ECCV 2018
Y2 - 8 September 2018 through 14 September 2018
ER -