On the Synergies Between Machine Learning and Binocular Stereo for Depth Estimation From Images : A Survey
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. R...
Veröffentlicht in: | IEEE transactions on pattern analysis and machine intelligence. - 1979. - 44(2022), 9 vom: 12. Sept., Seite 5314-5334 |
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Weitere Verfasser: | , , , |
Format: | Online-Aufsatz |
Sprache: | English |
Veröffentlicht: |
2022
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Zugriff auf das übergeordnete Werk: | IEEE transactions on pattern analysis and machine intelligence |
Schlagworte: | Journal Article Review Research Support, U.S. Gov't, Non-P.H.S. |
Zusammenfassung: | 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 |
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Beschreibung: | Date Completed 08.08.2022 Date Revised 14.09.2022 published: Print-Electronic Citation Status MEDLINE |
ISSN: | 1939-3539 |
DOI: | 10.1109/TPAMI.2021.3070917 |