AMatFormer: Efficient Feature Matching via Anchor Matching Transformer

TL;DR

AMatFormer introduces an efficient transformer-based feature matching method that uses anchor features to reduce redundancy and computational cost, improving matching accuracy across various benchmarks.

Contribution

The paper proposes a novel anchor-based attention mechanism in a transformer framework for feature matching, enhancing efficiency and robustness over existing methods.

Findings

Outperforms previous methods on multiple benchmarks

Reduces computational cost significantly

Achieves higher matching accuracy

Abstract

Learning based feature matching methods have been commonly studied in recent years. The core issue for learning feature matching is to how to learn (1) discriminative representations for feature points (or regions) within each intra-image and (2) consensus representations for feature points across inter-images. Recently, self- and cross-attention models have been exploited to address this issue. However, in many scenes, features are coming with large-scale, redundant and outliers contaminated. Previous self-/cross-attention models generally conduct message passing on all primal features which thus lead to redundant learning and high computational cost. To mitigate limitations, inspired by recent seed matching methods, in this paper, we propose a novel efficient Anchor Matching Transformer (AMatFormer) for the feature matching problem. AMatFormer has two main aspects: First, it mainly conducts self-/cross-attention on some anchor features and leverages these anchor features as message bottleneck to learn the representations for all primal features. Thus, it can be implemented efficiently and compactly. Second, AMatFormer adopts a shared FFN module to further embed the features of two images into the common domain and thus learn the consensus feature representations for the matching problem. Experiments on several benchmarks demonstrate the effectiveness and efficiency of the proposed AMatFormer matching approach.