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

arXiv: https://arxiv.org/abs/2004.08566 · HTML: https://mlsanity.com/paper/2004.08566/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Apr 2020.

## Built on by (most cited first, 8 of 8)

- [CroCo: Self-Supervised Pre-training for 3D Vision Tasks by Cross-View Completion](https://mlsanity.com/paper/2210.10716/) (2022)
- [SMD-Nets: Stereo Mixture Density Networks](https://mlsanity.com/paper/2104.03866/) (2021)
- [On the confidence of stereo matching in a deep-learning era: a quantitative evaluation](https://mlsanity.com/paper/2101.00431/) (2021)
- [Separable Convolutions for Optimizing 3D Stereo Networks](https://mlsanity.com/paper/2108.10216/) (2021)
- [Level Set Binocular Stereo with Occlusions](https://mlsanity.com/paper/2109.03464/) (2021)
- [Distilling Stereo Networks for Performant and Efficient Leaner Networks](https://mlsanity.com/paper/2503.18544/) (2025)
- [LeanStereo: A Leaner Backbone based Stereo Network](https://mlsanity.com/paper/2503.18557/) (2025)
- [Self-Supervised Perceptually Interpretable Monocular Depth Estimation](https://mlsanity.com/paper/2609.30987/) (2026)

## Abstract (arXiv)

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.