Recent years have witnessed enormous progress in AI-related fields such as computer
vision, machine learning, and autonomous vehicles. As with any rapidly growing field, it …

Augmented reality (AR) has proven to be an invaluable interactive medium to reduce
cognitive load by bridging the gap between the task-at-hand and relevant information by …

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 …

Abstract Visual Place Recognition (VPR) is a crucial part of mobile robotics and autonomous
driving as well as other computer vision tasks. It refers to the process of identifying a place …

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 …

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 present a novel method for local image feature matching. Instead of performing image
feature detection, description, and matching sequentially, we propose to first establish pixel …