13 citations · 23 across the 6 of their papers we have counts for
5 papers · 1 filter
RaCo: Ranking and Covariance for Practical Learned Keypoints
Abhiram Shenoi, Philipp Lindenberger, Paul-Edouard Sarlin +1
This paper introduces RaCo, a lightweight neural network designed to learn robust and versatile keypoints suitable for a variety of 3D computer vision tasks. The model integrates t…
Scaling Image Geo-Localization to Continent Level
Philipp Lindenberger, Paul-Edouard Sarlin, Jan Hosang +4
Determining the precise geographic location of an image at a global scale remains an unsolved challenge. Standard image retrieval techniques are inefficient due to the sheer volume…
GeoCalib: Learning Single-image Calibration with Geometric Optimization
Alexander Veicht, Paul-Edouard Sarlin, Philipp Lindenberger +1
From a single image, visual cues can help deduce intrinsic and extrinsic camera parameters like the focal length and the gravity direction. This single-image calibration can benefi…
LightGlue: Local Feature Matching at Light Speed
Philipp Lindenberger, Paul-Edouard Sarlin, Marc Pollefeys
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 m…
Pixel-Perfect Structure-from-Motion with Featuremetric Refinement
Philipp Lindenberger, Paul-Edouard Sarlin, Viktor Larsson +1
Finding local features that are repeatable across multiple views is a cornerstone of sparse 3D reconstruction. The classical image matching paradigm detects keypoints per-image onc…