Bridging Stereo Matching and Optical Flow via Spatiotemporal Correspondence

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

研究成果: Conference contribution同行評審

16 引文 斯高帕斯(Scopus)

摘要

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.

原文English
主出版物標題IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2019)
發行者IEEE Computer Society
頁面1890-1899
頁數10
DOIs
出版狀態Published - 六月 2019
事件32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019 - Long Beach, United States
持續時間: 16 六月 201920 六月 2019

出版系列

名字Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
2019-June
ISSN(列印)1063-6919

Conference

Conference32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019
國家United States
城市Long Beach
期間16/06/1920/06/19

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