Stereo matching aims to estimate the disparity between matching pixels in a stereo image pair, which is of great importance to robotics, autonomous driving, and other computer vision tasks. 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 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 carry out 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}.

翻译：立体匹配旨在估计立体图像对中匹配像素之间的视差，这对机器人学、自动驾驶及其他计算机视觉任务具有重要意义。尽管近年来涌现出大量令人瞩目的方法，但在复现其成果并确定最适合实际应用的架构方面仍面临挑战。为解决这一问题，本文提出了一个侧重于实际适用性而非单纯性能提升的综合基准。具体而言，我们开发了一套灵活高效的立体匹配代码库，命名为OpenStereo。OpenStereo包含10余种网络模型的训练与推理代码，据我们所知，它是目前最完整的立体匹配工具箱。基于OpenStereo，我们进行了实验，并已达成或超越了原论文中报告的性能指标。此外，通过全面的消融实验，我们对立体匹配领域的最新进展进行了详尽分析与解构。这些研究催生了强基线模型StereoBase的诞生。在已发表的方法中，我们的StereoBase在SceneFlow、KITTI 2015、KITTI 2012（反射表面）数据集上排名第一，且在所有评估指标上均取得了最优性能。同时，StereoBase具备强大的跨数据集泛化能力。代码已开源至\url{https://github.com/XiandaGuo/OpenStereo}。