We introduce LightGlue, a deep neural network that learns to match local features across
images. We revisit multiple design decisions of SuperGlue, the state of the art in sparse …

Generating robust and reliable correspondences across images is a fundamental task for a
diversity of applications. To capture context at both global and local granularity, we propose …

Line segments are powerful features complementary to points. They offer structural cues,
robust to drastic viewpoint and illumination changes, and can be present even in texture …

Abstract Neural Radiance Fields (NeRFs) have achieved impressive results in novel view
synthesis and surface reconstruction tasks. However, their performance suffers under …

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 …

Infrared–visible image registration and fusion are closely related processes, and it is an
attractive problem to implement coordinated registration and fusion in a unified framework …

Semantic 2D maps are commonly used by humans and machines for navigation purposes,
whether it's walking or driving. However, these maps have limitations: they lack detail, often …

We propose a structure-from-motion framework to recover accurate camera poses and point
clouds from unordered images. Traditional SfM systems typically rely on the successful …

Feature matching is an important computer vision task that involves estimating
correspondences between two images of a 3D scene and dense methods estimate all such …

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