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.
CITATION STYLE
Zhong, Y., Dai, Y., & Li, H. (2018). Stereo computation for a single mixture image. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11213 LNCS, pp. 441–456). Springer Verlag. https://doi.org/10.1007/978-3-030-01240-3_27
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