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Article Dans Une Revue IEEE Transactions on Image Processing Année : 2021

A Lightweight Depth Estimation Network for Wide-Baseline Light Fields

Résumé

Existing traditional and ConvNet-based methods for light field depth estimation mainly work on the narrow-baseline scenario. This paper explores the feasibility and capability of ConvNets to estimate depth in another promising scenario: wide-baseline light fields. Due to the deficiency of training samples, a large-scale and diverse synthetic wide-baseline dataset with labelled data is introduced for depth prediction tasks. Considering the practical goal for real-world applications, we design an end-to-end trained lightweight convolutional network to infer depths from light fields, called LLF-Net. The proposed LLF-Net is built by incorporating a cost volume which allows variable angular light field inputs and an attention module that enables to recover details at occlusion areas. Evaluations are made on the synthetic and real-world wide-baseline light fields, and experimental results show that the proposed network achieves the best performance when compared to recent state-of-the-art methods. We also evaluate our LLF-Net on narrow-baseline datasets, and it consequently improves the performance of previous methods.
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Dates et versions

hal-03163686 , version 1 (29-03-2021)

Identifiants

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Yan Li, Qiong Wang, Lu Zhang, Gauthier Lafruit. A Lightweight Depth Estimation Network for Wide-Baseline Light Fields. IEEE Transactions on Image Processing, 2021, 30, pp.2288-2300. ⟨10.1109/TIP.2021.3051761⟩. ⟨hal-03163686⟩
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