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 …

In this paper, we present a second order spatial compatibility (SC^ 2) measure based
method for efficient and robust point cloud registration (PCR), called SC^ 2-PCR. Firstly, we …

Abstract Graph Neural Networks (GNNs) with attention have been successfully applied for
learning visual feature matching. However, current methods learn with complete graphs …

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 …

Visual localization is a fundamental task for various applications including autonomous
driving and robotics. Prior methods focus on extracting large amounts of often redundant …

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 …

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

Multilayer perceptron (MLP) has become the de facto backbone in two-view correspondence
learning, for it can extract effective deep features from unordered correspondences …

Vision Transformer (ViT) has emerged as a competitive alternative to convolutional neural
networks for various computer vision applications. Specifically, ViTs' multi-head attention …