The DOI matches its Crossref record; 157 of the 157 references it cites were found in a registry or an index.
A Survey on Deep Learning Techniques for Stereo-Based Depth Estimation
Open accessAbstract
Estimating depth from RGB images is a long-standing ill-posed problem, which has been explored for decades by the computer vision, graphics, and machine learning communities. Among the existing techniques, stereo matching remains one of the most widely used in the literature due to its strong connection to the human binocular system. Traditionally, stereo-based depth estimation has been addressed through matching hand-crafted features across multiple images. Despite the extensive amount of research, these traditional techniques still suffer in the presence of highly textured areas, large uniform regions, and occlusions. Motivated by their growing success in solving various 2D and 3D vision problems, deep learning for stereo-based depth estimation has attracted a growing interest from the community, with more than 150 papers published in this area between 2014 and 2019. This new generation of methods has demonstrated a significant leap in performance, enabling applications such as autonomous driving and augmented reality. In this paper, we provide a comprehensive survey of this new and continuously growing field of research, summarize the most commonly used pipelines, and discuss their benefits and limitations. In retrospect of what has been achieved so far, we also conjecture what the future may hold for deep learning-based stereo for depth estimation research.
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Cites
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Hands-On Bayesian Neural Networks—A Tutorial for Deep Learning Users
Jospin, L. V., Laga, H., Boussaid, F., Buntine, W., & Bennamoun, M. (2022) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2022.3155327 · Cited by 879
Self-citationRevisiting Stereo Depth Estimation From a Sequence-to-Sequence Perspective with Transformers
Li, Z., Liu, X., Drenkow, N., Ding, A., Creighton, F. X., Taylor, R. H., & Unberath, M. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00614 · Cited by 335
CroCo v2: Improved Cross-view Completion Pre-training for Stereo Matching and Optical Flow
Weinzaepfel, P., Lucas, T., Leroy, V., Cabon, Y., Arora, V., Brégier, R., Csurka, G., Antsfeld, L., Chidlovskii, B., & Revaud, J. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01647 · Cited by 110
Metadata
- Authors
- Laga, H., Jospin, L. V., Boussaid, F., & Bennamoun, M.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2022-04-01
- Volume
- 44
- Issue
- 4
- Pages
- 1738-1764
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- 1939-3539
- OpenAlex
- W3033193681
- Cited by
- 336
- References
- 157
- FWCI
- 13.34
- Type
- journal article
- Language
- en
- Open access
- green
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