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16 May 2022
Open Access
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Published:
16 May 2022
Light field salient object detection: A review and benchmark
Tao Zhou3(
),
),
Cite this article:
Fu K, Jiang Y, Ji G-P, et al.
Light field salient object detection: A review and benchmark.
Computational Visual Media,
2022, 8(4): 509-534.
https://doi.org/10.1007/s41095-021-0256-2
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Salient object detection (SOD) is a long-standing research topic in computer vision with increasing interest in the past decade. Since light fields record comprehensive information of natural scenes that benefit SOD in a number of ways, using light field inputs to improve saliency detection over conventional RGB inputs is an emerging trend. This paper provides the first comprehensive review and a benchmark for light field SOD, which has long been lacking in the saliency community. Firstly, we introduce light fields, including theory and data forms, and then review existing studies on light field SOD, covering ten traditional models, seven deep learning-based models, a comparative study, and a brief review. Existing datasets for light field SOD are also summarized. Secondly, we benchmark nine representative light field SOD models together with several cutting-edge RGB-D SOD models on four widely used light field datasets, providing insightful discussions and analyses, including a comparison between light field SOD and RGB-D SOD models. Due to the inconsistency of current datasets, we further generate complete data and supplement focal stacks, depth maps, and multi-view images for them, making them consistent and uniform. Our supplemental data make a universal benchmark possible. Lastly, light field SOD is a specialised problem, because of its diverse data representations and high dependency on acquisition hardware, so it differs greatly from other saliency detection tasks. We provide nine observations on challenges and future directions, and outline several open issues. All the materials including models, datasets, benchmarking results, and supplemented light field datasets are publicly available at https://github.com/kerenfu/LFSOD-Survey.
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Light field salient object detection: A review and benchmark
Tao Zhou3(
),
),
Abstract
Salient object detection (SOD) is a long-standing research topic in computer vision with increasing interest in the past decade. Since light fields record comprehensive information of natural scenes that benefit SOD in a number of ways, using light field inputs to improve saliency detection over conventional RGB inputs is an emerging trend. This paper provides the first comprehensive review and a benchmark for light field SOD, which has long been lacking in the saliency community. Firstly, we introduce light fields, including theory and data forms, and then review existing studies on light field SOD, covering ten traditional models, seven deep learning-based models, a comparative study, and a brief review. Existing datasets for light field SOD are also summarized. Secondly, we benchmark nine representative light field SOD models together with several cutting-edge RGB-D SOD models on four widely used light field datasets, providing insightful discussions and analyses, including a comparison between light field SOD and RGB-D SOD models. Due to the inconsistency of current datasets, we further generate complete data and supplement focal stacks, depth maps, and multi-view images for them, making them consistent and uniform. Our supplemental data make a universal benchmark possible. Lastly, light field SOD is a specialised problem, because of its diverse data representations and high dependency on acquisition hardware, so it differs greatly from other saliency detection tasks. We provide nine observations on challenges and future directions, and outline several open issues. All the materials including models, datasets, benchmarking results, and supplemented light field datasets are publicly available at https://github.com/kerenfu/LFSOD-Survey.
Keywords:
deep learning, light field, benchmarking, salient object detection (SOD)
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Publication history
Received: 23 July 2021
Accepted: 03 October 2021
Published:
16 May 2022
Issue date: December 2022
Copyright
© The Author(s) 2022.
Acknowledgements
Keren Fu was supported by the National Natural Science Foundation of China (Nos. 62176169 and61703077) and SCU-Luzhou Municipal People’s Government Strategic Cooperation Project (No. 2020CDLZ-10). Tao Zhou was supported by the National Natural Science Foundation of China (No.62172228). Qijun Zhao was supported by the National Natural Science Foundation of China (No. 61773270).
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