Stereo matching is a fundamental task for 3D scene reconstruction. Recently, deep learning based methods have proven effective on some benchmark datasets, such as KITTI and Scene Flow. UAVs (Unmanned Aerial Vehicles) are commonly utilized for surface observation, and their captured images are frequently used for detailed 3D reconstruction due to high resolution and low-altitude acquisition. At present, the mainstream supervised learning network requires a significant amount of training data with ground-truth labels to learn model parameters. However, due to the scarcity of UAV stereo matching datasets, the learning-based network cannot be applied to UAV images. To facilitate further research, this paper proposes a novel pipeline to generate accurate and dense disparity maps using detailed meshes reconstructed by UAV images and LiDAR point clouds. Through the proposed pipeline, this paper constructs a multi-resolution UAV scenario dataset, called UAVStereo, with over 34k stereo image pairs covering 3 typical scenes. As far as we know, UAVStereo is the first stereo matching dataset of UAV low-altitude scenarios. The dataset includes synthetic and real stereo pairs to enable generalization from the synthetic domain to the real domain. Furthermore, our UAVStereo dataset provides multi-resolution and multi-scene images pairs to accommodate a variety of sensors and environments. In this paper, we evaluate traditional and state-of-the-art deep learning methods, highlighting their limitations in addressing challenges in UAV scenarios and offering suggestions for future research. The dataset is available at https://github.com/rebecca0011/UAVStereo.git

翻译：立体匹配是实现三维场景重建的基础任务。近年来，基于深度学习的立体匹配方法在KITTI和Scene Flow等基准数据集上展现出优异效果。无人机（UAV）因具备高分辨率与低空采集特性，被广泛应用于地表观测及三维精细重建。当前主流的有监督学习网络需要大量带真实标签的训练数据来学习模型参数，然而由于无人机立体匹配数据集的匮乏，基于学习的网络难以应用于无人机图像。为促进相关研究，本文提出了一种新颖的数据生成流程，通过无人机影像与激光雷达点云重建的精细网格模型，生成精确且稠密的视差图。利用该流程，我们构建了包含超过3.4万对立体图像、覆盖3类典型场景的多分辨率无人机场景数据集——UAVStereo。据我们所知，UAVStereo是首个面向无人机低空场景的立体匹配数据集，同时包含合成图像与真实图像以支持从合成域到真实域的泛化研究。此外，该数据集提供多分辨率与多场景图像对，能够适配多样化的传感器与使用环境。本文对传统方法与现有先进深度学习方法进行了评估，揭示了它们在应对无人机场景挑战时的局限性，并为未来研究提供了建议。数据集访问地址为：https://github.com/rebecca0011/UAVStereo.git