Raising the Ceiling: Conflict-Free Local Feature Matching with Dynamic View Switching

TL;DR

This paper introduces RCM, a novel feature matching method that enhances matchability and conflict resolution, significantly increasing ground-truth matches by 260% through dynamic view switching and conflict-free strategies.

Contribution

RCM is the first to combine dynamic view switching with conflict-free coarse matching, addressing key issues in feature matching and improving both accuracy and efficiency.

Findings

260% increase in ground-truth matches

Outperforms state-of-the-art methods in accuracy

Maintains high efficiency with coarse-to-fine architecture

Abstract

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.