The DOI matches its Crossref record; 222 of the 227 references it cites were found in a registry or an index.
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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The text we found for this paper looks like another document, so the self-check wasn’t run.
Citations
Cites
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404
Proceedings of IEEE Conference on Computer Vision and Pattern Recognition
(1993) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.1993.341010 · Cited by 14,475
Conditional Generative Adversarial Nets
Mirza, M., & Osindero, S. (2014) · arXiv · doi:10.48550/arxiv.1411.1784 · Cited by 8,863
Object Detection With Deep Learning: A Review
Zhao, Z.-Q., Zheng, P., Xu, S.-T., & Wu, X. (2019) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2018.2876865 · Cited by 5,545
Frequency-tuned salient region detection
Achanta, R., Hemami, S. S., Estrada, F., & Susstrunk, S. E. (2009) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2009.5206596 · Cited by 4,364
Residual Attention Network for Image Classification
Wang, F., Jiang, M., Qian, C., Yang, S., Li, C., Zhang, H., Wang, X., & Tang, X. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.683 · Cited by 4,073
Saliency Detection: A Spectral Residual Approach
Hou, X., & Zhang, L. (2007) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2007.383267 · Cited by 3,453
Unsupervised Monocular Depth Estimation with Left-Right Consistency
Godard, C., Aodha, O. M., & Brostow, G. J. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.699 · Cited by 3,270
Proceedings of the International Joint Conference on Artificial Intelligence 2007
Bundy, A. (2007) · International Joint Conference on Artificial Intelligence · Cited by 2,761
Saliency Detection via Graph-Based Manifold Ranking
Yang, C., Zhang, L., Lu, H., Ruan, X., & Yang, M.-H. (2013) · 2013 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2013.407 · Cited by 2,554
Research works printed but not linked: 1
- Lang, C. Y.; Nguyen, T. V.; Katti, H.; Yadati, K.; Kankanhalli, M.; Yan, S. C. Depth matters: influence of depth cues on visual saliency. In: Computer Vision — ECCV 2012. Lecture Notes in Computer Science, Vol. 7573. Fitzgibbon, A.; Lazebnik, S.; Perona, P.; Sato, Y.; Schmid, C. Eds. Springer Berlin Heidelberg, 101–115, 2012.
Cited by
Sorted by citations
Items 301–330 of 330
SFD-Net:Synergizing Feature Fusion with HQ-Depth Maps for RGB-D Salient Object Detection
Yan, Z., Yan, B., & Chen, S. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11228182
Transformer-Based Cross-Modal Feature Fusion Network for RGB-D Salient Object Detection
Zhang, Z., Chen, H., Xu, Q., & Chen, Q. (2024) · doi:10.1109/isdh64927.2024.00007
Li, G., Ai, W., Lv, J., Liu, B., Zou, Y., Ji, D., & Zhang, H. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.109296
CIHD-Net: A Cross-Modal Interactive Hierarchical Dilated Network for RGB-D Salient Object Detection
Long, Q., Hu, J., & Xu, Q. (2026) · Communications in computer and information science · doi:10.1007/978-981-95-3480-7_28
Graph-Based Uncertainty Modeling and Multimodal Fusion for Salient Object Detection
Xiong, Y., Shi, W., Wen, Y., & Liu, R. (2026) · Lecture notes in computer science · doi:10.1007/978-981-95-4445-5_9
Efficient Layer-Wise Cross-View Calibration and Aggregation for Multispectral Object Detection
He, X., Yang, T., Yan, T., Li, H., Ge, Y., Ren, Z., Liu, Z., Jiang, J., & Tang, C. (2026) · Electronics · doi:10.3390/electronics15030498
Probabilistic Indoor 3D Object Detection from RGB-D via Gaussian Distribution Estimation
Kim, H.-G. (2026) · Mathematics · doi:10.3390/math14030421
Salient Object Segmentation in 360° images/videos and light field
Zhang, Y. (2022) · theses.fr (ABES) · doi:10.70675/7b4980a9z109dz4ccez8cf5z0fef8b2c9cb6
RGB-D salient object detection via cross-modal adaptive correlation learning network
Ding, G., Fang, Y., & Yan, J. (2026) · Signal Processing Image Communication · doi:10.1016/j.image.2026.117564
DR-Net: Depth-assisted refinement network for image manipulation localization
Niu, Y., Zhu, X., Shi, Z., & Lyu, Y. (2026) · Neurocomputing · doi:10.1016/j.neucom.2026.133776
Xu, C., Li, Q., Zhao, S., & Li, H. (2026) · IEEE Transactions on Image Processing · doi:10.1109/tip.2026.3690334
RGB-thermal salient object detection: A survey
Chen, C., Li, S., Du, J., Liang, W., Wang, B., & Xu, J. (2026) · Computer Science Review · doi:10.1016/j.cosrev.2026.101001
Xiong, Z., & Li, Y. (2026) · Electronics Letters · doi:10.1049/ell2.70623
PAAMNet: Progressive attention and adaptive modulation network for V-D-T salient object detection
Chang, C., Li, Q., Li, X., Zhou, H., & Ye, Q. (2026) · Optics & Laser Technology · doi:10.1016/j.optlastec.2026.115686
Wang, Z., Shang, Z., Yang, X., & Zhou, Z. (2026) · Automation in Construction · doi:10.1016/j.autcon.2026.107121
Human intrusion detection based on LiDAR-camera fusion
Lin, L., Han, X., Wang, Y., & Yao, Z. (2026) · Measurement Science and Technology · doi:10.1088/1361-6501/ae8c61
RGB-D Camouflaged Object Detection via Multi-level Refinement Enriched Feature Pyramid Network
Modak, B., & Wang, M. (2026) · Lecture notes in computer science · doi:10.1007/978-981-92-3378-6_46
Qi, X., Liu, G., Su, C., & Wang, Y. (2026) · Displays · doi:10.1016/j.displa.2026.103626
Depth reliability-aware dual-stage network for RGB-D salient object segmentation
He, H., Huang, H., & Wang, Y. (2026) · Pattern Analysis and Applications · doi:10.1007/s10044-026-01743-7
Jia, M., & Bao, K. (2026) · IET Computers & Digital Techniques · doi:10.1049/cdt2/1592978
A novel “teacher–student” cross-modal fusion framework for salient object detection
Wang, Z., Zhang, Y., Xu, X., Jin, Z., Yang, D., Wang, Z., Xiong, M., & Meng, F. (2026) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2026.115628
SU-CAD: Lightweight Real-Time Anomaly Detection via Saliency Guidance
Li, W., Wen, G., Lei, Z., Wu, M., Wu, D., Tian, H., & He, J. (2026) · IEEE Sensors Journal · doi:10.1109/jsen.2026.3730828
Enhancing Salient Object Detection Through VGG16–VGG19 Architectures
Aljerbi, A., Ghariba, B., Marey, O. I., Masrub, A. A., & Arbi, A. (2026) · Tobruk University Journal of Engineering Sciences · doi:10.64516/tujes.v7i2.78
Liu, Y., Yin, S., Zhu, J., Liu, X., 蒋太祥, Bajić, I. V., & Yang, Y.-H. (2026) · Applied Soft Computing · doi:10.1016/j.asoc.2026.116534
Unbiased Dynamic Multimodal Fusion
Wei, S., Zhang, K., Chen, L., He, T., & Duan, G. (2026) · doi:10.1109/cvprf71909.2026.00599
Efficient depth-guided segmentation: lightweight high-resolution dichotomous image segmentation
Chen, Y., Chen, S., Wang, S., Chen, F., & Yang, X. (2026) · The Visual Computer · doi:10.1007/s00371-026-04777-4
Wu, N., Daixian, 朱. Z., Liu, L., Chen, X., & Wei, M. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7656371
Wu, F., Liu, H., Liang, J., Hou, Z., Cheng, Y., & Wang, K. (2026) · Cluster Computing · doi:10.1007/s10586-026-06632-6
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 ›
Are you an author, or do you see a problem with this page? Request a review
