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 …

Multi-view stereo reconstruction (MVS) in the wild requires to first estimate the camera
intrinsic and extrinsic parameters. These are usually tedious and cumbersome to obtain yet …

We present a novel method for efficiently producing semi-dense matches across images.
Previous detector-free matcher LoFTR has shown remarkable matching capability in …

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 …

Image Matching is a core component of all best-performing algorithms and pipelines in 3D
vision. Yet despite matching being fundamentally a 3D problem, intrinsically linked to …

We introduce a lightweight and accurate architecture for resource-efficient visual
correspondence. Our method dubbed XFeat (Accelerated Features) revisits fundamental …

Abstract Structure-from-motion (SfM) is a long-standing problem in the computer vision
community which aims to reconstruct the camera poses and 3D structure of a scene from a …

The image matching field has been witnessing a continuous emergence of novel learnable
feature matching techniques with ever-improving performance on conventional benchmarks …

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 …

We introduce SEA-RAFT, a more simple, efficient, and accurate RAFT for optical flow.
Compared with RAFT, SEA-RAFT is trained with a new loss (mixture of Laplace). It directly …