# Co-Teaching: An Ark to Unsupervised Stereo Matching

arXiv: https://arxiv.org/abs/2107.08186 · HTML: https://mlsanity.com/paper/2107.08186/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 22 references that are in the map.
- First posted on arXiv 17 Jul 2021.

## Builds on (most cited first, 17 of 17)

- [A Closer Look at Memorization in Deep Networks](https://mlsanity.com/paper/1706.05394/) (2017)
- [A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation](https://mlsanity.com/paper/1512.02134/) (2015)
- [Pyramid Stereo Matching Network](https://mlsanity.com/paper/1803.08669/) (2018)
- [Group-wise Correlation Stereo Network](https://mlsanity.com/paper/1903.04025/) (2019)
- [Hierarchical Neural Architecture Search for Deep Stereo Matching](https://mlsanity.com/paper/2010.13501/) (2020)
- [Learning by Analogy: Reliable Supervision from Transformations for Unsupervised Optical Flow Estimation](https://mlsanity.com/paper/2003.13045/) (2020)
- [Occlusion Aware Unsupervised Learning of Optical Flow](https://mlsanity.com/paper/1711.05890/) (2017)
- [SNE-RoadSeg: Incorporating Surface Normal Information into Semantic Segmentation for Accurate Freespace Detection](https://mlsanity.com/paper/2008.11351/) (2020)
- [Flow2Stereo: Effective Self-Supervised Learning of Optical Flow and Stereo Matching](https://mlsanity.com/paper/2004.02138/) (2020)
- [Road surface 3d reconstruction based on dense subpixel disparity map estimation](https://mlsanity.com/paper/1807.01874/) (2018)
- [Self-Supervised Learning for Stereo Matching with Self-Improving Ability](https://mlsanity.com/paper/1709.00930/) (2017)
- [PVStereo: Pyramid Voting Module for End-to-End Self-Supervised Stereo Matching](https://mlsanity.com/paper/2103.07094/) (2021)
- [Dynamic Fusion Module Evolves Drivable Area and Road Anomaly Detection: A Benchmark and Algorithms](https://mlsanity.com/paper/2103.02433/) (2021)
- [Applying Surface Normal Information in Drivable Area and Road Anomaly Detection for Ground Mobile Robots](https://mlsanity.com/paper/2008.11383/) (2020)
- [Learning Collision-Free Space Detection from Stereo Images: Homography Matrix Brings Better Data Augmentation](https://mlsanity.com/paper/2012.07890/) (2020)
- [Self-Supervised Drivable Area and Road Anomaly Segmentation using RGB-D Data for Robotic Wheelchairs](https://mlsanity.com/paper/2007.05950/) (2020)
- [CoT-AMFlow: Adaptive Modulation Network with Co-Teaching Strategy for Unsupervised Optical Flow Estimation](https://mlsanity.com/paper/2011.02156/) (2020)

## Built on by (most cited first, 1 of 1)

- [Integrating Disparity Confidence Estimation into Relative Depth Prior-Guided Unsupervised Stereo Matching](https://mlsanity.com/paper/2508.01275/) (2025)

## Abstract (arXiv)

Stereo matching is a key component of autonomous driving perception. Recent unsupervised stereo matching approaches have received adequate attention due to their advantage of not requiring disparity ground truth. These approaches, however, perform poorly near occlusions. To overcome this drawback, in this paper, we propose CoT-Stereo, a novel unsupervised stereo matching approach. Specifically, we adopt a co-teaching framework where two networks interactively teach each other about the occlusions in an unsupervised fashion, which greatly improves the robustness of unsupervised stereo matching. Extensive experiments on the KITTI Stereo benchmarks demonstrate the superior performance of CoT-Stereo over all other state-of-the-art unsupervised stereo matching approaches in terms of both accuracy and speed. Our project webpage is https://sites.google.com/view/cot-stereo.