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A Framework for Learning Depth From a Flexible Subset of Dense and Sparse Light Field Views.

Authors
  • Shi, Jinglei
  • Jiang, Xiaoran
  • Guillemot, Christine
Type
Published Article
Journal
IEEE Transactions on Image Processing
Publisher
Institute of Electrical and Electronics Engineers
Publication Date
Dec 01, 2019
Volume
28
Issue
12
Pages
5867–5880
Identifiers
DOI: 10.1109/TIP.2019.2923323
PMID: 31247553
Source
Medline
Language
English
License
Unknown

Abstract

In this paper, we propose a learning-based depth estimation framework suitable for both densely and sparsely sampled light fields. The proposed framework consists of three processing steps: initial depth estimation, fusion with occlusion handling, and refinement. The estimation can be performed from a flexible subset of input views. The fusion of initial disparity estimates, relying on two warping error measures, allows us to have an accurate estimation in occluded regions and along the contours. In contrast with methods relying on the computation of cost volumes, the proposed approach does not need any prior information on the disparity range. Experimental results show that the proposed method outperforms state-of-the-art light fields depth estimation methods, including prior methods based on deep neural architectures.

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