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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 commonlyutilized for surface observation, and their captured images are frequently usedfor detailed 3D reconstruction due to high resolution and low-altitudeacquisition. At present, the mainstream supervised learning network requires asignificant amount of training data with ground-truth labels to learn modelparameters. However, due to the scarcity of UAV stereo matching datasets, thelearning-based network cannot be applied to UAV images. To facilitate furtherresearch, this paper proposes a novel pipeline to generate accurate and densedisparity maps using detailed meshes reconstructed by UAV images and LiDARpoint clouds. Through the proposed pipeline, this paper constructs amulti-resolution UAV scenario dataset, called UAVStereo, with over 34k stereoimage pairs covering 3 typical scenes. As far as we know, UAVStereo is thefirst stereo matching dataset of UAV low-altitude scenarios. The datasetincludes synthetic and real stereo pairs to enable generalization from thesynthetic domain to the real domain. Furthermore, our UAVStereo datasetprovides multi-resolution and multi-scene images pairs to accommodate a varietyof sensors and environments. In this paper, we evaluate traditional andstate-of-the-art deep learning methods, highlighting their limitations inaddressing challenges in UAV scenarios and offering suggestions for futureresearch. The dataset is available athttps://github.com/rebecca0011/UAVStereo.git

UAVStereo: A Multiple Resolution Dataset for Stereo Matching in UAV Scenarios