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Elias Mueggler
Researcher at University of Zurich
Publications - 30
Citations - 3034
Elias Mueggler is an academic researcher from University of Zurich. The author has contributed to research in topics: Event (computing) & Visual odometry. The author has an hindex of 20, co-authored 30 publications receiving 2056 citations. Previous affiliations of Elias Mueggler include ETH Zurich.
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Journal ArticleDOI
The event-camera dataset and simulator: Event-based data for pose estimation, visual odometry, and SLAM:
TL;DR: In this article, the authors proposed a dynamic and active-pixel vision sensor DAVIS, which incorporated a conventional global-shutter camera and an event-based sensor in the same pixel array.
Posted Content
The Replica Dataset: A Digital Replica of Indoor Spaces.
Julian Straub,Thomas Whelan,Lingni Ma,Yufan Chen,Erik Wijmans,Simon Green,Jakob Engel,Raul Mur-Artal,Carl Yuheng Ren,Shobhit Verma,Anton Clarkson,Yan Mingfei,Brian Budge,Yajie Yan,Xiaqing Pan,June Yon,Yuyang Zou,Kimberly Leon,Nigel Carter,Jesus Briales,Tyler Gillingham,Elias Mueggler,Luis Pesqueira,Manolis Savva,Dhruv Batra,Hauke Strasdat,Renzo De Nardi,Michael Goesele,Steven Lovegrove,Richard Newcombe +29 more
TL;DR: Replica, a dataset of 18 highly photo-realistic 3D indoor scene reconstructions at room and building scale, is introduced to enable machine learning (ML) research that relies on visually, geometrically, and semantically realistic generative models of the world.
Proceedings ArticleDOI
Event-based, 6-DOF Pose Tracking for High-Speed Maneuvers
TL;DR: This paper presents the first onboard perception system for 6-DOF localization during high-speed maneuvers using a Dynamic Vision Sensor (DVS), and provides a versatile method to capture ground-truth data using a DVS.
Journal ArticleDOI
Autonomous, Vision-based Flight and Live Dense 3D Mapping with a Quadrotor Micro Aerial Vehicle
Matthias Faessler,Flavio Fontana,Christian Forster,Elias Mueggler,Matia Pizzoli,Davide Scaramuzza +5 more
TL;DR: A vision‐based quadrotor micro aerial vehicle that can autonomously execute a given trajectory and provide a live, dense three‐dimensional map of an area and the practical challenges and lessons learned are discussed.
Journal ArticleDOI
EMVS: Event-Based Multi-View Stereo—3D Reconstruction with an Event Camera in Real-Time
TL;DR: This work introduces the problem of event-based multi-view stereo (EMVS) for event cameras and proposes a solution that elegantly exploits two inherent properties of an event camera: its ability to respond to scene edges—which naturally provide semi-dense geometric information without any pre-processing operation— and the fact that it provides continuous measurements as the sensor moves.