# WHU-Stereo: A Challenging Benchmark for Stereo Matching of High-Resolution Satellite Images

arXiv: https://arxiv.org/abs/2206.02342 · HTML: https://mlsanity.com/paper/2206.02342/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 41 references that are in the map.
- First posted on arXiv 6 Jun 2022.

## Builds on (most cited first, 14 of 14)

- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation](https://mlsanity.com/paper/1512.02134/) (2015)
- [TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems](https://mlsanity.com/paper/1603.04467/) (2016)
- [Pyramid Stereo Matching Network](https://mlsanity.com/paper/1803.08669/) (2018)
- [Deeper Depth Prediction with Fully Convolutional Residual Networks](https://mlsanity.com/paper/1606.00373/) (2016)
- [End-to-End Learning of Geometry and Context for Deep Stereo Regression](https://mlsanity.com/paper/1703.04309/) (2017)
- [Stereo Matching by Training a Convolutional Neural Network to Compare Image Patches](https://mlsanity.com/paper/1510.05970/) (2015)
- [StereoNet: Guided Hierarchical Refinement for Real-Time Edge-Aware Depth Prediction](https://mlsanity.com/paper/1807.08865/) (2018)
- [Semantic Stereo for Incidental Satellite Images](https://mlsanity.com/paper/1811.08739/) (2018)
- [What Makes Good Synthetic Training Data for Learning Disparity and Optical Flow Estimation?](https://mlsanity.com/paper/1801.06397/) (2018)
- [A New Stereo Benchmarking Dataset for Satellite Images](https://mlsanity.com/paper/1907.04404/) (2019)
- [Left-Right Comparative Recurrent Model for Stereo Matching](https://mlsanity.com/paper/1804.00796/) (2018)
- [ResDepth: A Deep Residual Prior For 3D Reconstruction From High-resolution Satellite Images](https://mlsanity.com/paper/2106.08107/) (2021)

## Built on by (most cited first, 9 of 9)

- [An evaluation of Deep Learning based stereo dense matching dataset shift from aerial images and a large scale stereo dataset](https://mlsanity.com/paper/2402.12522/) (2024)
- [Unsupervised Stereo Matching Network For VHR Remote Sensing Images Based On Error Prediction](https://mlsanity.com/paper/2408.07419/) (2024)
- [Deep S2P: Integrating Learning Based Stereo Matching Into the Satellite Stereo Pipeline](https://mlsanity.com/paper/2603.21882/) (2026)
- [Rethinking the Key Factors for the Generalization of Remote Sensing Stereo Matching Networks](https://mlsanity.com/paper/2408.07613/) (2024)
- [Analysis of different disparity estimation techniques on aerial stereo image datasets](https://mlsanity.com/paper/2410.06711/) (2024)
- [Integrating Disparity Confidence Estimation into Relative Depth Prior-Guided Unsupervised Stereo Matching](https://mlsanity.com/paper/2508.01275/) (2025)
- [EpiMask: Leveraging Epipolar Distance Based Masks in Cross-Attention for Satellite Image Matching](https://mlsanity.com/paper/2603.21463/) (2026)
- [SeasonStereo: Robust Dense Stereo Matching for Multi-Date Satellite Imagery via Generative AI](https://mlsanity.com/paper/2607.27139/) (2026)
- [SatUnreal: A High-Precision Synthetic Dataset for Satellite Stereo Matching via Unreal Engine](https://mlsanity.com/paper/2609.27442/) (2026)

## Abstract (arXiv)

Stereo matching of high-resolution satellite images (HRSI) is still a fundamental but challenging task in the field of photogrammetry and remote sensing. Recently, deep learning (DL) methods, especially convolutional neural networks (CNNs), have demonstrated tremendous potential for stereo matching on public benchmark datasets. However, datasets for stereo matching of satellite images are scarce. To facilitate further research, this paper creates and publishes a challenging dataset, termed WHU-Stereo, for stereo matching DL network training and testing. This dataset is created by using airborne LiDAR point clouds and high-resolution stereo imageries taken from the Chinese GaoFen-7 satellite (GF-7). The WHU-Stereo dataset contains more than 1700 epipolar rectified image pairs, which cover six areas in China and includes various kinds of landscapes. We have assessed the accuracy of ground-truth disparity maps, and it is proved that our dataset achieves comparable precision compared with existing state-of-the-art stereo matching datasets. To verify its feasibility, in experiments, the hand-crafted SGM stereo matching algorithm and recent deep learning networks have been tested on the WHU-Stereo dataset. Experimental results show that deep learning networks can be well trained and achieves higher performance than hand-crafted SGM algorithm, and the dataset has great potential in remote sensing application. The WHU-Stereo dataset can serve as a challenging benchmark for stereo matching of high-resolution satellite images, and performance evaluation of deep learning models. Our dataset is available at https://github.com/Sheng029/WHU-Stereo