Video stereo matching is the task of estimating consistent disparity maps from rectified stereo videos. There is considerable scope for improvement in both datasets and methods within this area. Recent learning-based methods often focus on optimizing performance for independent stereo pairs, leading to temporal inconsistencies in videos. Existing video methods typically employ sliding window operation over time dimension, which can result in low-frequency oscillations corresponding to the window size. To address these challenges, we propose a bidirectional alignment mechanism for adjacent frames as a fundamental operation. Building on this, we introduce a novel video processing framework, BiDAStereo, and a plugin stabilizer network, BiDAStabilizer, compatible with general image-based methods. Regarding datasets, current synthetic object-based and indoor datasets are commonly used for training and benchmarking, with a lack of outdoor nature scenarios. To bridge this gap, we present a realistic synthetic dataset and benchmark focused on natural scenes, along with a real-world dataset captured by a stereo camera in diverse urban scenes for qualitative evaluation. Extensive experiments on in-domain, out-of-domain, and robustness evaluation demonstrate the contribution of our methods and datasets, showcasing improvements in prediction quality and achieving state-of-the-art results on various commonly used benchmarks. The project page, demos, code, and datasets are available at: https://tomtomtommi.github.io/BiDAVideo/.

翻译：视频立体匹配是从校正后的立体视频中估计一致视差图的任务。该领域在数据集和方法两方面均有显著改进空间。现有基于学习的方法常专注于优化独立立体对的表现，导致视频中出现时间不一致性。当前视频方法通常采用时间维度上的滑动窗口操作，这会引发与窗口大小对应的低频振荡。为解决这些挑战，我们提出一种将相邻帧双向对齐机制作为基础操作的方法。基于此，我们引入新型视频处理框架BiDAStereo，以及与通用图像方法兼容的插件稳定器网络BiDAStabilizer。在数据集方面，当前常用的合成物体数据集和室内数据集主要用于训练和基准测试，缺乏室外自然场景。为弥补这一空白，我们提出聚焦自然场景的真实感合成数据集与基准，以及由立体相机在多样化城市场景中采集的真实世界数据集用于定性评估。通过域内、跨域及鲁棒性评估的广泛实验，我们的方法与数据集展现出对预测质量的提升，并在多个常用基准上取得最先进结果。项目页面、演示、代码及数据集均发布于：https://tomtomtommi.github.io/BiDAVideo/。