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RGB-D salient object detection: A survey
Open accessAbstract
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.
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Metadata
- Authors
- Zhou, T., Fan, D.-P., Cheng, M.-M., Shen, J., & Shao, L.
- Affiliations
- Inception Institute of Artificial Intelligence (AE)
- Nankai University (CN)
- Corresponding author
- Fan, Deng-Ping
- Venue
- Computational Visual Media
- Publisher
- Tsinghua University Press
- Published
- 2021-03
- Volume
- 7
- Issue
- 1
- Pages
- 37-69
- ISSN
- 2096-0433 (ISSN-L)
- 2096-0662
- PMC
- PMC7788385
- OpenAlex
- W3046597754
- Cited by
- 331
- References
- 227
- Citations by year
- 2026: 33 · 2025: 42 · 2024: 50 · 2023: 70 · 2022: 76 · 2021: 50
- FWCI
- 3.76
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Computational Visual Media (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Natural Science Foundation of China (61922046, 2018AAA0100400)
- Natural Science Foundation of Tianjin City (17JCJQJC43700)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Keywords
- salient object detection, RGB-D, depth images, light field, benchmark dataset, visual perception, depth sensors
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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