Toward Free-Form Local Feature Matching.

Abstract

Existing feature matching methods are strongly coupled to their pre-defined position priors. For instance, sparse matchers are coupled to keypoints, and semi-dense matchers are coupled to grids. The coupled position prior dictates the distribution of matching points and imposes inherent limitations on the matcher. Consequently, sparse matchers suffer from a reliance on keypoint repeatability, while semi-dense matchers lack texture-based precision. Our preliminary work RCM leverages the keypoint prior in the source image and the grid prior in the target image, ensuring texture-based precision with keypoints while eliminating reliance on repeatability. However, RCM still relies heavily on keypoints in the source image, inheriting limitations such as sparsity and poor distribution in challenging scenes. To address these challenges, we introduce RCM+, which presents a novel free-form matching paradigm. By combining a position-agnostic encoder with a parameter-free decoder, we decouple the matcher from any position prior. As a result, the free-form matcher can match arbitrary input positions in a zero-shot manner, including detected keypoints, lines, edges, grids of any resolution, user-specified points, and more. This paradigm offers exceptional flexibility, allowing users to select position priors based on scene properties without retraining. Thus, RCM+ can leverage the advantages of various position priors without over-relying on any single prior, avoiding limitations in specific scenarios. To better match multiple position priors, we propose the Balancer, which reconciles all input position priors to achieve a more favorable point distribution for downstream tasks. Additionally, we enhance the view switcher and conflict-free matching layer introduced in RCM, further improving matching quality. Comprehensive experiments demonstrate the excellent performance, efficiency, and flexibility of RCM+, underscoring its promising potential for applications.