Bridging Stereo Matching and Optical Flow via Spatiotemporal Correspondence

Hsueh Ying Lai, Yi Hsuan Tsai, Wei-Chen Chiu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

Stereo matching and flow estimation are two essential tasks for scene understanding, spatially in 3D and temporally in motion. Existing approaches have been focused on the unsupervised setting due to the limited resource to obtain the large-scale ground truth data. To construct a self-learnable objective, co-related tasks are often linked together to form a joint framework. However, the prior work usually utilizes independent networks for each task, thus not allowing to learn shared feature representations across models. In this paper, we propose a single and principled network to jointly learn spatiotemporal correspondence for stereo matching and flow estimation, with a newly designed geometric connection as the unsupervised signal for temporally adjacent stereo pairs. We show that our method performs favorably against several state-of-the-art baselines for both unsupervised depth and flow estimation on the KITTI benchmark dataset.

Original languageEnglish
Title of host publicationIEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2019)
PublisherIEEE Computer Society
Pages1890-1899
Number of pages10
DOIs
StatePublished - Jun 2019
Event32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019 - Long Beach, United States
Duration: 16 Jun 201920 Jun 2019

Publication series

NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Volume2019-June
ISSN (Print)1063-6919

Conference

Conference32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019
CountryUnited States
CityLong Beach
Period16/06/1920/06/19

Keywords

  • Deep Learning
  • Scene Analysis and Understanding

Fingerprint Dive into the research topics of 'Bridging Stereo Matching and Optical Flow via Spatiotemporal Correspondence'. Together they form a unique fingerprint.

  • Cite this

    Lai, H. Y., Tsai, Y. H., & Chiu, W-C. (2019). Bridging Stereo Matching and Optical Flow via Spatiotemporal Correspondence. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2019) (pp. 1890-1899). [8953227] (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition; Vol. 2019-June). IEEE Computer Society. https://doi.org/10.1109/CVPR.2019.00199