Open AccessImproved Depth Estimation for Occlusion Scenes Using a Light-Field Camera
Author(s) -
Changkun Yang,
Zhaoqin Liu,
Kaichang Di,
Chenggang Hu,
Yexin Wang,
W. Y. Liang
Publication year - 2020
Publication title -
photogrammetric engineering and remote sensing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.483
H-Index - 127
eISSN - 2374-8079
pISSN - 0099-1112
DOI - 10.14358/pers.86.7.443
Subject(s) - light field , computer vision , artificial intelligence , computer science , field (mathematics) , pixel , consistency (knowledge bases) , identification (biology) , occlusion , mathematics , medicine , botany , pure mathematics , cardiology , biology
With the development of light-field imaging technology, depth estimation using light-field cameras has become a hot topic in recent years. Even through many algorithms have achieved good performance for depth estimation using light-field cameras, removing the influence of occlusion, especially multi-occlusion, is still a challenging task. The photo-consistency assumption does not hold in the presence of occlusions, which makes most depth estimation of light-field imaging unreliable. In this article, a novel method to handle complex occlusion in depth estimation of light-field imaging is proposed. The method can effectively identify occluded pixels using a refocusing algorithm, accurately select unoccluded views using the adaptive unoccluded-view identification algorithm, and then improve the depth estimation by computing the cost volumes in the unoccluded views. Experimental results demonstrate the advantages of our proposed algorithm compared with conventional state-of-the art algorithms on both synthetic and real light-field data sets.