RGB-D salient object detection: A survey

Tao Zhou; Deng-Ping Fan; Ming-Ming Cheng; Jianbing Shen; Ling Shao

doi:10.1007/s41095-020-0199-z

. 2021 Jan 7;7(1):37–69. doi: 10.1007/s41095-020-0199-z

RGB-D salient object detection: A survey

Tao Zhou ¹, Deng-Ping Fan ^1,^✉, Ming-Ming Cheng ², Jianbing Shen ¹, Ling Shao ¹

PMCID: PMC7788385 PMID: 33432275

Abstract

Salient object detection, which simulates human visual perception in locating the most significant object(s) in a scene, has been widely applied to various computer vision tasks. Now, the advent of depth sensors means that depth maps can easily be captured; this additional spatial information can boost the performance of salient object detection. Although various RGB-D based salient object detection models with promising performance have been proposed over the past several years, an in-depth understanding of these models and the challenges in this field remains lacking. In this paper, we provide a comprehensive survey of RGB-D based salient object detection models from various perspectives, and review related benchmark datasets in detail. Further, as light fields can also provide depth maps, we review salient object detection models and popular benchmark datasets from this domain too. Moreover, to investigate the ability of existing models to detect salient objects, we have carried out a comprehensive attribute-based evaluation of several representative RGB-D based salient object detection models. Finally, we discuss several challenges and open directions of RGB-D based salient object detection for future research. All collected models, benchmark datasets, datasets constructed for attribute-based evaluation, and related code are publicly available at https://github.com/taozh2017/RGBD-SODsurvey.

Keywords: RGB-D, saliency, light fields, benchmarks

Acknowledgements

This research was supported by a Major Project for a New Generation of AI under Grant No. 2018AAA0100400, National Natural Science Foundation of China (61922046), and Tianjin Natural Science Foundation (17JCJQJC43700).

Footnotes

Tao Zhou received his Ph.D. degree in pattern recognition and intelligent systems from the Institute of Image Processing and Pattern Recognition, Shanghai JiaoTong University in 2016. He is currently a research scientist at the Inception Institute of Artificial Intelligence, Abu Dhabi, United Arab Emirates. His research interests include machine learning, computer vision, and medical image analysis.

Deng-Ping Fan received his Ph.D. degree from Nankai University in 2019. He joined the Inception Institute of Artificial Intelligence in 2019. He has published about 20 papers in leading journals and conferences such as CVPR and ICCV. His research interests include computer vision, deep learning, and saliency detection.

Ming-Ming Cheng received his Ph.D. degree from Tsinghua University in 2012. He then was a research fellow for 2 years with Prof. Philip Torr in Oxford. He is now a professor at Nankai University, leading the Media Computing Lab. His research interests include computer graphics, machine learning, computer vision, and image processing. He is an Associate Editor of IEEE TIP. He has received several research awards, including an ACM China Rising Star Award, and an IBM Global SUR Award.

Jianbing Shen is currently the Lead Scientist of the Inception Institute of Artificial Intelligence. He is also a full professor at the School of Computer Science, Beijing Institute of Technology. He has published about 100 journal and conference papers in places such as IEEE TPAMI, CVPR, and ICCV. His research interests include computer vision and deep learning. He is an Associate Editor of IEEE TNNLS, IEEE TIP, etc.

Ling Shao is the CEO and Chief Scientist of the Inception Institute of Artificial Intelligence. His research interests include computer vision, machine learning, and medical imaging. He is an Associate Editor of IEEE TIP, IEEE TNNLS, and several other journals. He is a Fellow of the International Association of Pattern Recognition, the Institution of Engineering and Technology, and the British Computer Society.

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RGB-D salient object detection: A survey

Tao Zhou

Deng-Ping Fan

Ming-Ming Cheng

Jianbing Shen

Ling Shao

Abstract

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PERMALINK

RGB-D salient object detection: A survey

Tao Zhou

Deng-Ping Fan

Ming-Ming Cheng

Jianbing Shen

Ling Shao

Abstract

Acknowledgements

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