Kavli Affiliate: Zheng Zhu
| First 5 Authors: Xianda Guo, Juntao Lu, Chenming Zhang, Yiqi Wang, Yiqun Duan
| Summary:
Stereo matching, a pivotal technique in computer vision, plays a crucial role
in robotics, autonomous navigation, and augmented reality. Despite the
development of numerous impressive methods in recent years, replicating their
results and 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 performance enhancement. Specifically, we develop a flexible and efficient
stereo matching codebase, called OpenStereo. OpenStereo includes training and
inference codes of more than 12 network models, making it, to our knowledge,
the most complete stereo matching toolbox available. Based on OpenStereo, we
conducted experiments on the SceneFlow dataset and have achieved or surpassed
the performance metrics reported in the original paper. Additionally, we
conduct an in-depth revisitation of recent developments in stereo matching
through ablative experiments. These investigations inspired the creation of
StereoBase, a simple yet strong baseline model. Our extensive comparative
analyses of StereoBase against numerous contemporary stereo matching methods on
the SceneFlow dataset demonstrate its remarkably strong performance. The source
code is available at https://github.com/XiandaGuo/OpenStereo.
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