Dense feature matching is an important computer vision task that involves
estimating all correspondences between two images of a 3D scene. In this paper,
we revisit robust losses for matching from a Markov chain perspective, yielding
theoretical insights and large gains in performance. We begin by constructing a
unifying formulation of matching as a Markov chain, based on which we identify
two key stages which we argue should be decoupled for matching. The first is
the coarse stage, where the estimated result needs to be globally consistent.
The second is the refinement stage, where the model needs precise localization
capabilities. Inspired by the insight that these stages concern distinct
issues, we propose a coarse matcher following the regression-by-classification
paradigm that provides excellent globally consistent, albeit not exactly
localized, matches. This is followed by a local feature refinement stage using
well-motivated robust regression losses, yielding extremely precise matches.
Our proposed approach, which we call RoMa, achieves significant improvements
compared to the state-of-the-art. Code is available at
this https URL

从马尔可夫链的角度重新审视强健损失，将匹配问题分解为粗匹配和细匹配两个阶段，提出了一种 RoMa 算法以进行本地特征精炼，实现了显著的性能提升。