150 citations · 221 across the 15 of their papers we have counts for
18 papers
VidMap: Exploiting Temporal Structure for Video-Based Structure-from-Motion
Zador Pataki, Paul-Edouard Sarlin, Marc Pollefeys
Accurately recovering the camera's calibration and metric poses for any unconstrained video would unlock large-scale training data for navigation and scene understanding. The domin…
UNIGEOCLIP: Unified Geospatial Contrastive Learning
Guillaume Astruc, Eduard Trulls, Jan Hosang +2
The growing availability of co-located geospatial data spanning aerial imagery, street-level views, elevation models, text, and geographic coordinates offers a unique opportunity f…
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…
Benchmarking Egocentric Visual-Inertial SLAM at City Scale
Anusha Krishnan, Shaohui Liu, Paul-Edouard Sarlin +6
Precise 6-DoF simultaneous localization and mapping (SLAM) from onboard sensors is critical for wearable devices capturing egocentric data, which exhibits specific challenges, such…
MP-SfM: Monocular Surface Priors for Robust Structure-from-Motion
Zador Pataki, Paul-Edouard Sarlin, Johannes L. Schönberger +1
While Structure-from-Motion (SfM) has seen much progress over the years, state-of-the-art systems are prone to failure when facing extreme viewpoint changes in low-overlap, low-par…
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…
AffineGlue: Joint Matching and Robust Estimation
Daniel Barath, Dmytro Mishkin, Luca Cavalli +3
We propose AffineGlue, a method for joint two-view feature matching and robust estimation that reduces the combinatorial complexity of the problem by employing single-point minimal…
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…
SNAP: Self-Supervised Neural Maps for Visual Positioning and Semantic Understanding
Paul-Edouard Sarlin, Eduard Trulls, Marc Pollefeys +2
Semantic 2D maps are commonly used by humans and machines for navigation purposes, whether it's walking or driving. However, these maps have limitations: they lack detail, often co…
OrienterNet: Visual Localization in 2D Public Maps with Neural Matching
Paul-Edouard Sarlin, Daniel DeTone, Tsun-Yi Yang +7
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 expensiv…
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…
The Fishyscapes Benchmark: Measuring Blind Spots in Semantic Segmentation
Hermann Blum, Paul-Edouard Sarlin, Juan Nieto +2
Deep learning has enabled impressive progress in the accuracy of semantic segmentation. Yet, the ability to estimate uncertainty and detect failure is key for safety-critical appli…
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…