Stereo computation for a single mixture image

Yiran Zhong*, Yuchao Dai, Hongdong Li

*Corresponding author for this work

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    2 Citations (Scopus)

    Abstract

    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.

    Original languageEnglish
    Title of host publicationComputer Vision – ECCV 2018 - 15th European Conference, 2018, Proceedings
    EditorsMartial Hebert, Vittorio Ferrari, Cristian Sminchisescu, Yair Weiss
    PublisherSpringer Verlag
    Pages441-456
    Number of pages16
    ISBN (Print)9783030012397
    DOIs
    Publication statusPublished - 2018
    Event15th European Conference on Computer Vision, ECCV 2018 - Munich, Germany
    Duration: 8 Sept 201814 Sept 2018

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume11213 LNCS
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Conference

    Conference15th European Conference on Computer Vision, ECCV 2018
    Country/TerritoryGermany
    CityMunich
    Period8/09/1814/09/18

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