An Improved RaftStereo Trained with A Mixed Dataset for the Robust Vision Challenge 2022

• URL: http://arxiv.org/abs/2210.12785v1
• Date: Sun, 23 Oct 2022 17:01:34 GMT
• Title: An Improved RaftStereo Trained with A Mixed Dataset for the Robust Vision Challenge 2022
• Abstract summary: This report presents an improved RaftStereo trained with a mixed dataset of seven public datasets for a robust vision challenge. When evaluated on the training sets of Middlebury, KITTI-2015, and ETH3D, the model outperforms its counterparts trained with only one dataset. It ranks at 2nd place on the stereo leaderboard, demonstrating the benefits of mixed dataset pre-training.
• Score: 11.360379033636598
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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