Light Field Salient Object Detection: A Review and Benchmark
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Salient object detection (SOD) is a long-standing research topic in computer vision and has drawn an increasing amount of research interest in the past decade. This paper provides the first comprehensive review and benchmark for SOD on light field, which has long been lacking in the saliency community. Firstly, we introduce the preliminary knowledge of light field including theory and data forms, and then review existing studies on light field SOD, covering ten traditional models, six deep learning-based models, one comparative study, and one brief review. Existing datasets for light field SOD are summarized with detailed information and statistical analysis. Secondly, we benchmark seven representative light field SOD models together with several cutting-edge RGB-D SOD models on four widely used light field datasets, from which insightful discussions and analyses including the comparison between light field SOD and RGB-D SOD models are achieved. Besides, due to the inconsistency of datasets in their current forms, we further generate complete data and supplement focal stacks, depth maps and multi-view images for the inconsistent datasets, making them consistent and unified. Our supplemented data makes a universal benchmark possible. Lastly, because light field SOD is a quite special problem attributed to its diverse data representations and high dependency on acquisition hardware, making it differ greatly from other saliency detection tasks, we provide nine hints into the challenges and future directions, and outline several open issues. We hope our review and benchmarking could serve as a catalyst to advance research in this field. All the materials including collected models, datasets, benchmarking results, and supplemented light field datasets will be publicly available at our project site.
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