# On the confidence of stereo matching in a deep-learning era: a quantitative evaluation

arXiv: https://arxiv.org/abs/2101.00431 · HTML: https://mlsanity.com/paper/2101.00431/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 63 references that are in the map.
- First posted on arXiv 2 Jan 2021.

## Builds on (most cited first, 10 of 10)

- [A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation](https://mlsanity.com/paper/1512.02134/) (2015)
- [GA-Net: Guided Aggregation Net for End-to-end Stereo Matching](https://mlsanity.com/paper/1904.06587/) (2019)
- [Stereo Matching by Training a Convolutional Neural Network to Compare Image Patches](https://mlsanity.com/paper/1510.05970/) (2015)
- [Real-time self-adaptive deep stereo](https://mlsanity.com/paper/1810.05424/) (2018)
- [Learning to Adapt for Stereo](https://mlsanity.com/paper/1904.02957/) (2019)
- [Unsupervised Domain Adaptation for Depth Prediction from Images](https://mlsanity.com/paper/1909.03943/) (2019)
- [Improved Stereo Matching with Constant Highway Networks and Reflective Confidence Learning](https://mlsanity.com/paper/1701.00165/) (2016)
- [On the Synergies between Machine Learning and Binocular Stereo for Depth Estimation from Images: a Survey](https://mlsanity.com/paper/2004.08566/) (2020)
- [Using Self-Contradiction to Learn Confidence Measures in Stereo Vision](https://mlsanity.com/paper/1604.05132/) (2016)
- [CNN-based Cost Volume Analysis as Confidence Measure for Dense Matching](https://mlsanity.com/paper/1905.07287/) (2019)

## Built on by (most cited first, 23 of 23)

- [Learning the Distribution of Errors in Stereo Matching for Joint Disparity and Uncertainty Estimation](https://mlsanity.com/paper/2304.00152/) (2023)
- [A Survey on Deep Stereo Matching in the Twenties](https://mlsanity.com/paper/2407.07816/) (2024)
- [Digging Into Uncertainty-based Pseudo-label for Robust Stereo Matching](https://mlsanity.com/paper/2307.16509/) (2023)
- [Neural Disparity Refinement for Arbitrary Resolution Stereo](https://mlsanity.com/paper/2110.15367/) (2021)
- [Unsupervised confidence for LiDAR depth maps and applications](https://mlsanity.com/paper/2210.03118/) (2022)
- [RGB-Multispectral Matching: Dataset, Learning Methodology, Evaluation](https://mlsanity.com/paper/2206.07047/) (2022)
- [PointFix: Learning to Fix Domain Bias for Robust Online Stereo Adaptation](https://mlsanity.com/paper/2207.13340/) (2022)
- [These Maps Are Made by Propagation: Adapting Deep Stereo Networks to Road Scenarios with Decisive Disparity Diffusion](https://mlsanity.com/paper/2411.03717/) (2024)
- [Unleashing the Temporal Potential of Stereo Event Cameras for Continuous-Time 3D Object Detection](https://mlsanity.com/paper/2508.02288/) (2025)
- [Depth-aware Volume Attention for Texture-less Stereo Matching](https://mlsanity.com/paper/2402.08931/) (2024)
- [NTIRE 2025 Challenge on HR Depth from Images of Specular and Transparent Surfaces](https://mlsanity.com/paper/2506.05815/) (2025)
- [WarpRF: Multi-View Consistency for Training-Free Uncertainty Quantification and Applications in Radiance Fields](https://mlsanity.com/paper/2506.22433/) (2025)
- [Modeling Stereo-Confidence Out of the End-to-End Stereo-Matching Network via Disparity Plane Sweep](https://mlsanity.com/paper/2401.12001/) (2024)
- [Robust Confidence Intervals in Stereo Matching using Possibility Theory](https://mlsanity.com/paper/2404.06273/) (2024)
- [Active Stereo in the Wild through Virtual Pattern Projection](https://mlsanity.com/paper/2406.04345/) (2024)
- [MCPDepth: Omnidirectional Depth Estimation via Stereo Matching from Multi-Cylindrical Panoramas](https://mlsanity.com/paper/2408.01653/) (2024)
- [Robust and Flexible Omnidirectional Depth Estimation with Multiple 360-degree Cameras](https://mlsanity.com/paper/2409.14766/) (2024)
- [CMD: Constraining Multimodal Distribution for Domain Adaptation in Stereo Matching](https://mlsanity.com/paper/2504.21302/) (2025)
- [Stextsuperscript2Mtextsuperscript2: Scalable Stereo Matching Model for Reliable Depth Estimation](https://mlsanity.com/paper/2507.13229/) (2025)
- [Uncertainty Quantification Framework for Aerial and UAV Photogrammetry through Error Propagation](https://mlsanity.com/paper/2507.13486/) (2025)
- [Integrating Disparity Confidence Estimation into Relative Depth Prior-Guided Unsupervised Stereo Matching](https://mlsanity.com/paper/2508.01275/) (2025)
- [Revisiting Matching Response and Swept Feature Volumes for Wide-baseline Omnidirectional Stereo](https://mlsanity.com/paper/2607.11097/) (2026)
- [A Calibration Audit of Confidence in Feed-Forward 3D Reconstruction Models](https://mlsanity.com/paper/2608.29705/) (2026)

## Abstract (arXiv)

Stereo matching is one of the most popular techniques to estimate dense depth maps by finding the disparity between matching pixels on two, synchronized and rectified images. Alongside with the development of more accurate algorithms, the research community focused on finding good strategies to estimate the reliability, i.e. the confidence, of estimated disparity maps. This information proves to be a powerful cue to naively find wrong matches as well as to improve the overall effectiveness of a variety of stereo algorithms according to different strategies. In this paper, we review more than ten years of developments in the field of confidence estimation for stereo matching. We extensively discuss and evaluate existing confidence measures and their variants, from hand-crafted ones to the most recent, state-of-the-art learning based methods. We study the different behaviors of each measure when applied to a pool of different stereo algorithms and, for the first time in literature, when paired with a state-of-the-art deep stereo network. Our experiments, carried out on five different standard datasets, provide a comprehensive overview of the field, highlighting in particular both strengths and limitations of learning-based strategies.