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|a 10.1109/TPAMI.2021.3070917
|2 doi
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|a pubmed24n1079.xml
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|a DE-627
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|e rakwb
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|a eng
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| 100 |
1 |
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|a Poggi, Matteo
|e verfasserin
|4 aut
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| 245 |
1 |
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|a On the Synergies Between Machine Learning and Binocular Stereo for Depth Estimation From Images
|b A Survey
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|c 2022
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
|b c
|2 rdamedia
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|a ƒa Online-Ressource
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|2 rdacarrier
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| 500 |
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|a Date Completed 08.08.2022
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|a Date Revised 14.09.2022
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|a published: Print-Electronic
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|a Citation Status MEDLINE
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|a 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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|a Journal Article
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|a Review
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| 650 |
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|a Research Support, U.S. Gov't, Non-P.H.S.
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| 700 |
1 |
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|a Tosi, Fabio
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Batsos, Konstantinos
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Mordohai, Philippos
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Mattoccia, Stefano
|e verfasserin
|4 aut
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| 773 |
0 |
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|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 44(2022), 9 vom: 12. Sept., Seite 5314-5334
|w (DE-627)NLM098212257
|x 1939-3539
|7 nnns
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|g volume:44
|g year:2022
|g number:9
|g day:12
|g month:09
|g pages:5314-5334
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| 856 |
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|u http://dx.doi.org/10.1109/TPAMI.2021.3070917
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