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A Lightweight Depth Estimation Network for Wide-Baseline Light Fields
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DOI：10.1109/TIP.2021.3051761.png)
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摘要
En 中文
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.
Keyword:
Estimation
Feature extraction
Training
Cameras
Streaming media
Human computer interaction
Convolution
Light field
depth estimation
convolutional neural network
lightweight
wide-baseline
narrow-baseline
synthetic dataset
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IF:
13.7
论文数:
1.0W
被引数:
8.4W
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