Stereo Computation for a Single Mixture Image
This addresses a severely ill-posed problem in computer vision for applications like 3D reconstruction, but it is an incremental advancement in deep learning-based stereo methods.
The paper tackles the novel problem of stereo computation from a single mixture image, aiming to separate it into left and right stereo layers and recover a disparity map, achieving effective results as demonstrated in experiments.
This paper proposes an original problem of \emph{stereo computation from a single 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.