@inproceedings{3b1bd5a618d343be9d66af85f2ec410d,
title = "Light field scene flow with occlusion regularization",
abstract = "The scene flow provides a comprehensive understanding of the vision field's 3D dynamics, which is extremely useful for most computer vision applications. Stereo vision has been the conventional means for scene flow estimation. However, light field camera provides a more convenient and reliable solution for the same task with its unique advantage in scene depth estimation. In this work, we propose a joint estimation framework for the optical flow and depth flow given two light field images as input. The scene depth estimated from the light field will be used for occlusion detection and sparse correspondence regularization which results in a more robust estimation of optical flow. Depth flow will be calculated based on the interpolated correspondence matching. Experiments show that the proposed framework can produce competitive scene flow estimation at a much lower computational cost, compared to state-of-The-Art methods.",
keywords = "depth flow, Light field, occlusion, optical flow, scene flow",
author = "Jie Chen and Yun Ni and Juhui Hou and Chau, {Lap Pui}",
note = "Funding Information: ACKNOWLEDGMENT The research was partially supported by the ST Engineering-NTU Corporate Lab through the NRF corporate lab@university scheme. Publisher Copyright: {\textcopyright} 2017 IEEE.; 9th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2017 ; Conference date: 12-12-2017 Through 15-12-2017",
year = "2018",
month = feb,
day = "5",
doi = "10.1109/APSIPA.2017.8282200",
language = "English",
series = "Proceedings - 9th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1148--1151",
booktitle = "Proceedings - 9th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2017",
}