Modeling sparse and dense image matching within a unified functional
correspondence model has recently attracted increasing research interest.
However, existing efforts mainly focus on improving matching accuracy while
ignoring its efficiency, which is crucial for realworld applications. In this
paper, we propose an efficient structure named Efficient Correspondence
Transformer (ECO-TR) by finding correspondences in a coarse-to-fine manner,
which significantly improves the efficiency of functional correspondence model.
To achieve this, multiple transformer blocks are stage-wisely connected to
gradually refine the predicted coordinates upon a shared multi-scale feature
extraction network. Given a pair of images and for arbitrary query coordinates,
all the correspondences are predicted within a single feed-forward pass. We
further propose an adaptive query-clustering strategy and an uncertainty-based
outlier detection module to cooperate with the proposed framework for faster
and better predictions. Experiments on various sparse and dense matching tasks
demonstrate the superiority of our method in both efficiency and effectiveness
against existing state-of-the-arts.

本文提出了一种名为 Efficient Correspondence Transformer (ECO-TR) 的高效对应变换器，通过一个由多个 transformer blocks 组成的网络，以从粗到细的方式找到对应关系，并提出了一种自适应查询聚类策略和基于不确定性的异常值检测模块来协同实现更快、更好的预测，实验结果表明了本方法在效率和效果方面的优越性。