Dynamic stereo matching is the task of estimating consistent disparities from
stereo videos with dynamic objects. Recent learning-based methods prioritize
optimal performance on a single stereo pair, resulting in temporal
inconsistencies. Existing video methods apply per-frame matching and
window-based cost aggregation across the time dimension, leading to
low-frequency oscillations at the scale of the window size. Towards this
challenge, we develop a bidirectional alignment mechanism for adjacent frames
as a fundamental operation. We further propose a novel framework, BiDAStereo,
that achieves consistent dynamic stereo matching. Unlike the existing methods,
we model this task as local matching and global aggregation. Locally, we
consider correlation in a triple-frame manner to pool information from adjacent
frames and improve the temporal consistency. Globally, to exploit the entire
sequence's consistency and extract dynamic scene cues for aggregation, we
develop a motion-propagation recurrent unit. Extensive experiments demonstrate
the performance of our method, showcasing improvements in prediction quality
and achieving state-of-the-art results on various commonly used benchmarks.

基于学习的方法在动态对象的立体视频中优化单个立体对的有力表现，但存在时间上的不一致性。本文提出了一种双向对齐机制和一个新的框架 BiDAStereo，通过局部匹配和全局聚合的方式实现了一致的动态立体匹配，从而在多个常用基准测试中展示了预测质量的提高和最先进的结果。