RGB-D salient object detection: A survey

By Tao Zhou, Deng-Ping Fan, Ming-Ming Cheng, Jianbing Shen, Ling Shao. Computational Visual Media, 2020

304 citations recorded by the source corpus.

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.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/adbca0a1532f749db08c9607422864aa4e157b67

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Deng-Ping Fan
2. Ling Shao
3. Ming-Ming Cheng
4. Jianbing Shen
5. Huchuan Lu
6. Ali Borji
7. Wenguan Wang
8. Nian Liu

Citations in the corpus

• Salient Object Detection via Integrity Learning (2021, background): “The complete history of multi-modal SOD can be found in the survey [63] and benchmark [2] works.”
• Structure-Measure: A New Way to Evaluate Foreground Maps (2017, background): “Finally, we also evaluate our measure over SOD dataset, which is a subset of the BSDS dataset.”
• Cross-level Feature Aggregation Network for Polyp Segmentation (2023, unknown): “Additionally, four metrics are introduced, which are widely used in the field of object detection [32,52,53] , including S-measure ( S α ) [54] , F-measure [55] ( F w β ), E φ [56] , and mean absolute error ( M ) [57] .”
• Artificial intelligence in the creative industries: a review (2020, background): “More details on the recent SOD on RGB-D data can be found in (Zhou et al. 2021).”