An evaluation of Deep Learning based stereo dense matching dataset shift
  from aerial images and a large scale stereo dataset
Teng Wu Bruno Vallet Marc Pierrot-Deseilligny Ewelina Rupnik
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.