OpenStereo: A Comprehensive Benchmark for Stereo Matching and Strong Baseline

Xianda Guo, Chenming Zhang, Juntao Lu, Yiqun Duan, Yiqi Wang, Tian Yang, Zheng Zhu, Long Chen

Keywords: stereo matching

Abstract:

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. To address 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 \textbf{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 \textbf{StereoBase}, a strong baseline model. Our StereoBase ranks 1\textsuperscript{st} on SceneFlow, KITTI 2015, 2012 (Reflective) among published methods and performs best across all metrics. In addition, StereoBase has strong cross-dataset generalization.

Primary Area: datasets and benchmarks

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Submission Number: 6658