Stereo Computation for a Single Mixture Image

Yiran Zhong, Yuchao Dai, Hongdong Li; Proceedings of the European Conference on Computer Vision (ECCV), 2018, pp. 435-450


This paper proposes an original problem of emph{stereo computation from a single (additive) mixture image}-- a challenging problem that had not been researched before. The goal is to separate (ie 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 (ie, 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.

Related Material

[pdf] [arXiv]
author = {Zhong, Yiran and Dai, Yuchao and Li, Hongdong},
title = {Stereo Computation for a Single Mixture Image},
booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
month = {September},
year = {2018}