  Stereo-matching is a fundamental problem in computer vision. Despite recent
progress by deep learning, improving the robustness is ineluctable when
deploying stereo-matching models to real-world applications. Different from the
common practices, i.e., developing an elaborate model to achieve robustness, we
argue that collecting multiple available datasets for training is a cheaper way
to increase generalization ability. Specifically, this report presents an
improved RaftStereo trained with a mixed dataset of seven public datasets for
the robust vision challenge (denoted as iRaftStereo_RVC). When evaluated on the
training sets of Middlebury, KITTI-2015, and ETH3D, the model outperforms its
counterparts trained with only one dataset, such as the popular Sceneflow.
After fine-tuning the pre-trained model on the three datasets of the challenge,
it ranks at 2nd place on the stereo leaderboard, demonstrating the benefits of
mixed dataset pre-training.
