  Current feature matching methods prioritize improving modeling capabilities
to better align outputs with ground-truth matches, which are the theoretical
upper bound on matching results, metaphorically depicted as the "ceiling".
However, these enhancements fail to address the underlying issues that directly
hinder ground-truth matches, including the scarcity of matchable points in
small scale images, matching conflicts in dense methods, and the
keypoint-repeatability reliance in sparse methods. We propose a novel feature
matching method named RCM, which Raises the Ceiling of Matching from three
aspects. 1) RCM introduces a dynamic view switching mechanism to address the
scarcity of matchable points in source images by strategically switching image
pairs. 2) RCM proposes a conflict-free coarse matching module, addressing
matching conflicts in the target image through a many-to-one matching strategy.
3) By integrating the semi-sparse paradigm and the coarse-to-fine architecture,
RCM preserves the benefits of both high efficiency and global search,
mitigating the reliance on keypoint repeatability. As a result, RCM enables
more matchable points in the source image to be matched in an exhaustive and
conflict-free manner in the target image, leading to a substantial 260%
increase in ground-truth matches. Comprehensive experiments show that RCM
exhibits remarkable performance and efficiency in comparison to
state-of-the-art methods.
