NCNet: Learning to Find Non-Consistent Correspondence Using Learnable Frequency Response Function

Abstract

False correspondence removal is a persistent challenge in image feature-matching-based applications, especially in complex scenes. Traditional methods often rely on the consistency assumption to model the motion of correct correspondences, which neglects non-consistent correct correspondences, resulting in suboptimal performance. In this paper, we introduce the NCNet, a novel network designed to address this limitation by fitting the motion of both consistent and non-consistent correct correspondences using the learnable frequency response function. Unlike conventional approaches that focus on pixel-based 2D movements, NCNet utilizes the Motion Fitting Residual Module to estimate the high-dimensional motion, capturing the intricate 3D geometrical variations of matching pairs. To further enhance performance, we propose a loss function that balances the performance between the two types of correct correspondences. Extensive experiments demonstrate that NCNet significantly outperforms existing methods in terms of precision for false correspondence removal. The implementation of our approach is publicly available at: https://github.com/Livsdjo/NCNet-Code.