3 citations · 6 across the 3 of their papers we have counts for
6 papers
LaMAR: Benchmarking Localization and Mapping for Augmented Reality
Paul-Edouard Sarlin, Mihai Dusmanu, Johannes L. Schönberger +5
Localization and mapping is the foundational technology for augmented reality (AR) that enables sharing and persistence of digital content in the real world. While significant prog…
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…
Back to the Feature: Learning Robust Camera Localization from Pixels to Pose
Paul-Edouard Sarlin, Ajaykumar Unagar, Måns Larsson +8
Camera pose estimation in known scenes is a 3D geometry task recently tackled by multiple learning algorithms. Many regress precise geometric quantities, like poses or 3D points, f…
SuperGlue: Learning Feature Matching with Graph Neural Networks
Paul-Edouard Sarlin, Daniel DeTone, Tomasz Malisiewicz +1
This paper introduces SuperGlue, a neural network that matches two sets of local features by jointly finding correspondences and rejecting non-matchable points. Assignments are est…
From Coarse to Fine: Robust Hierarchical Localization at Large Scale
Paul-Edouard Sarlin, Cesar Cadena, Roland Siegwart +1
Robust and accurate visual localization is a fundamental capability for numerous applications, such as autonomous driving, mobile robotics, or augmented reality. It remains, howeve…
Leveraging Deep Visual Descriptors for Hierarchical Efficient Localization
Paul-Edouard Sarlin, Frédéric Debraine, Marcin Dymczyk +2
Many robotics applications require precise pose estimates despite operating in large and changing environments. This can be addressed by visual localization, using a pre-computed 3…