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 …

We use neural radiance fields (NeRFs) to build interactive 3D environments from large-scale
visual captures spanning buildings or even multiple city blocks collected primarily from …

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 …

Camera pose estimation is a long-standing computer vision problem that to date often relies
on classical methods, such as handcrafted keypoint matching, RANSAC and bundle …

Abstract We propose Deep Patch Visual Odometry (DPVO), a new deep learning system for
monocular Visual Odometry (VO). DPVO uses a novel recurrent network architecture …

Local feature matching is a computationally intensive task at the subpixel level. While
detector-based methods coupled with feature descriptors struggle in low-texture scenes …

Novel view synthesis has seen major advances in recent years, with 3D Gaussian splatting
offering an excellent level of visual quality, fast training and real-time rendering. However …

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 …

Recovering 3D structure and camera motion from images has been a long-standing focus of
computer vision research and is known as Structure-from-Motion (SfM). Solutions to this …

Humans can orient themselves in their 3D environments using simple 2D maps. Differently,
algorithms for visual localization mostly rely on complex 3D point clouds that are expensive …