# An evaluation of Deep Learning based stereo dense matching dataset shift from aerial images and a large scale stereo dataset

arXiv: https://arxiv.org/abs/2402.12522 · HTML: https://mlsanity.com/paper/2402.12522/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 27 references that are in the map.
- First posted on arXiv 19 Feb 2024.

## Builds on (most cited first, 11 of 11)

- [The ApolloScape Open Dataset for Autonomous Driving and its Application](https://mlsanity.com/paper/1803.06184/) (2018)
- [Hierarchical Neural Architecture Search for Deep Stereo Matching](https://mlsanity.com/paper/2010.13501/) (2020)
- [Stereo Matching by Training a Convolutional Neural Network to Compare Image Patches](https://mlsanity.com/paper/1510.05970/) (2015)
- [Domain-invariant Stereo Matching Networks](https://mlsanity.com/paper/1911.13287/) (2019)
- [Semantic Stereo for Incidental Satellite Images](https://mlsanity.com/paper/1811.08739/) (2018)
- [A Review on Deep Learning in UAV Remote Sensing](https://mlsanity.com/paper/2101.10861/) (2021)
- [Unsupervised Domain Adaptation for Depth Prediction from Images](https://mlsanity.com/paper/1909.03943/) (2019)
- [Continuous 3D Label Stereo Matching using Local Expansion Moves](https://mlsanity.com/paper/1603.08328/) (2016)
- [Embedded real-time stereo estimation via Semi-Global Matching on the GPU](https://mlsanity.com/paper/1610.04121/) (2016)
- [WHU-Stereo: A Challenging Benchmark for Stereo Matching of High-Resolution Satellite Images](https://mlsanity.com/paper/2206.02342/) (2022)
- [A Comparative Study on Deep-Learning Methods for Dense Image Matching of Multi-angle and Multi-date Remote Sensing Stereo Images](https://mlsanity.com/paper/2210.14031/) (2022)

## Built on by (most cited first, 2 of 2)

- [BRDF-NeRF: Neural Radiance Fields with Optical Satellite Images and BRDF Modelling](https://mlsanity.com/paper/2409.12014/) (2024)
- [Multi-view dense image matching with similarity learning and geometry priors](https://mlsanity.com/paper/2505.11264/) (2025)

## Abstract (arXiv)

Dense matching is crucial for 3D scene reconstruction since it enables the recovery of scene 3D geometry from image acquisition. Deep Learning (DL)-based methods have shown effectiveness in the special case of epipolar stereo disparity estimation in the computer vision community. DL-based methods depend heavily on the quality and quantity of training datasets. However, generating ground-truth disparity maps for real scenes remains a challenging task in the photogrammetry community. To address this challenge, we propose a method for generating ground-truth disparity maps directly from Light Detection and Ranging (LiDAR) and images to produce a large and diverse dataset for six aerial datasets across four different areas and two areas with different resolution images. We also introduce a LiDAR-to-image co-registration refinement to the framework that takes special precautions regarding occlusions and refrains from disparity interpolation to avoid precision loss. Evaluating 11 dense matching methods across datasets with diverse scene types, image resolutions, and geometric configurations, which are deeply investigated in dataset shift, GANet performs best with identical training and testing data, and PSMNet shows robustness across different datasets, and we proposed the best strategy for training with a limit dataset. We will also provide the dataset and training models; more information can be found at https://github.com/whuwuteng/Aerial_Stereo_Dataset.