An evaluation of Deep Learning based stereo dense matching dataset shift from aerial images and a large scale stereo dataset
This addresses dataset scarcity for dense matching in photogrammetry, though it is incremental as it builds on existing methods for data generation.
The paper tackled the challenge of generating ground-truth disparity maps for real aerial scenes by proposing a method using LiDAR and images to create a large, diverse dataset, and found that GANet performed best with identical training and testing data while PSMNet showed robustness across datasets.
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.