Feature matching is a challenging computer vision task that involves finding
correspondences between two images of a 3D scene. In this paper we consider the dense …

Local feature matching aims at establishing sparse correspondences between a pair of
images. Recently, detector-free methods present generally better performance but are not …

We propose a new method, named curvature similarity extractor (CSE), for improving local
feature matching across images. CSE calculates the curvature of the local 3D surface patch …

Learning robust local image feature matching is a fundamental low-level vision task, which
has been widely explored in the past few years. Recently, detector-free local feature …

Matching multimodal remote sensing images (RSIs) remains an ongoing challenge due to
the significant nonlinear radiometric differences and geometric distortions, resulting in …

This paper introduces a Transformer-based integrative feature and cost aggregation network
designed for dense matching tasks. In the context of dense matching, many works benefit …

Local feature matching enjoys wide-ranging applications in the realm of computer vision,
encompassing domains such as image retrieval, 3D reconstruction, and object recognition …

Local feature matching has been a critical task in computer vision applications. In the early
days of computer vision, local feature matching relied heavily on detector-based methods …

Establishing accurate correspondences between aerial and ground images is facing
immense challenges because of the drastic viewpoint, illumination, and scale variations …

We tackle the efficiency problem of learning local feature matching. Recent advancements
have given rise to purely CNN-based and transformer-based approaches, each augmented …