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On the Synergies Between Machine Learning and Binocular Stereo for Depth Estimation From Images: A Survey.
- Source :
-
IEEE Transactions on Pattern Analysis & Machine Intelligence . Sep2022, Vol. 44 Issue 9, p5314-5334. 21p. - Publication Year :
- 2022
-
Abstract
- Stereo matching is one of the longest-standing problems in computer vision with close to 40 years of studies and research. Throughout the years the paradigm has shifted from local, pixel-level decision to various forms of discrete and continuous optimization to data-driven, learning-based methods. Recently, the rise of machine learning and the rapid proliferation of deep learning enhanced stereo matching with new exciting trends and applications unthinkable until a few years ago. Interestingly, the relationship between these two worlds is two-way. While machine, and especially deep, learning advanced the state-of-the-art in stereo matching, stereo itself enabled new ground-breaking methodologies such as self-supervised monocular depth estimation based on deep networks. In this paper, we review recent research in the field of learning-based depth estimation from single and binocular images highlighting the synergies, the successes achieved so far and the open challenges the community is going to face in the immediate future. [ABSTRACT FROM AUTHOR]
- Subjects :
- *MACHINE learning
*DEEP learning
*COMPUTER vision
*OPTICAL radar
*MONOCULARS
Subjects
Details
- Language :
- English
- ISSN :
- 01628828
- Volume :
- 44
- Issue :
- 9
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Pattern Analysis & Machine Intelligence
- Publication Type :
- Academic Journal
- Accession number :
- 158406126
- Full Text :
- https://doi.org/10.1109/TPAMI.2021.3070917