On the Synergies between Machine Learning and Binocular Stereo for Depth Estimation from Images: a Survey

• URL: http://arxiv.org/abs/2004.08566v2
• Date: Wed, 31 Mar 2021 14:00:23 GMT
• Title: On the Synergies between Machine Learning and Binocular Stereo for Depth Estimation from Images: a Survey
• Authors: Matteo Poggi, Fabio Tosi, Konstantinos Batsos, Philippos Mordohai, Stefano Mattoccia
• Abstract summary: Stereo matching is one of the longest-standing problems in computer vision with close to 40 years of studies and research. Recent research in the field of learning-based depth estimation from single and binocular images highlight the successes achieved so far.
• Score: 45.08733033427528
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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