Match-Stereo-Videos: Bidirectional Alignment for Consistent Dynamic
  Stereo Matching
Junpeng Jing Ye Mao Krystian Mikolajczyk
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.