Kavli Affiliate: Zheng Zhu
| First 5 Authors: Xianda Guo, Chenming Zhang, Juntao Lu, Yiqun Duan, Yiqi Wang
| Summary:
Stereo matching aims to estimate the disparity between matching pixels in a
stereo image pair, which is important to robotics, autonomous driving, and
other computer vision tasks. Despite the development of numerous impressive
methods in recent years, determining the most suitable architecture for
practical application remains challenging. Addressing this gap, our paper
introduces a comprehensive benchmark focusing on practical applicability rather
than solely on individual models for optimized performance. Specifically, we
develop a flexible and efficient stereo matching codebase, called OpenStereo.
OpenStereo includes training and inference codes of more than 10 network
models, making it, to our knowledge, the most complete stereo matching toolbox
available. Based on OpenStereo, we conducted experiments and have achieved or
surpassed the performance metrics reported in the original paper. Additionally,
we conduct an exhaustive analysis and deconstruction of recent developments in
stereo matching through comprehensive ablative experiments. These
investigations inspired the creation of StereoBase, a strong baseline model.
Our StereoBase ranks 1st on SceneFlow, KITTI 2015, 2012 (Reflective) among
published methods and achieves the best performance across all metrics. In
addition, StereoBase has strong cross-dataset generalization. Code is available
at url{https://github.com/XiandaGuo/OpenStereo}.
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