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2020 – today
- 2024
[j8]Daniel Gehrig
, Davide Scaramuzza:
Low-latency automotive vision with event cameras. Nat. 629(8014): 1034-1040 (2024)- [j7]Mohammed Salah, Abdulla Ayyad, Muhammad Humais, Daniel Gehrig, Abdelqader Abusafieh, Lakmal D. Seneviratne, Davide Scaramuzza, Yahya H. Zweiri:
E-Calib: A Fast, Robust, and Accurate Calibration Toolbox for Event Cameras. IEEE Trans. Image Process. 33: 3977-3990 (2024) - [c19]Asude Aydin, Mathias Gehrig, Daniel Gehrig, Davide Scaramuzza:
A Hybrid ANN-SNN Architecture for Low-Power and Low-Latency Visual Perception. CVPR Workshops 2024: 5701-5711 - [c18]Ling Gao, Daniel Gehrig, Hang Su, Davide Scaramuzza, Laurent Kneip:
An N-Point Linear Solver for Line and Motion Estimation with Event Cameras. CVPR 2024: 14596-14605 - [i26]Ling Gao, Daniel Gehrig, Hang Su, Davide Scaramuzza, Laurent Kneip:
An N-Point Linear Solver for Line and Motion Estimation with Event Cameras. CoRR abs/2404.00842 (2024) - [i25]Royina Karegoudra Jayanth, Yinshuang Xu, Ziyun Wang, Evangelos Chatzipantazis, Daniel Gehrig, Kostas Daniilidis:
EqNIO: Subequivariant Neural Inertial Odometry. CoRR abs/2408.06321 (2024) - 2023
- [c17]Ling Gao, Hang Su, Daniel Gehrig, Marco Cannici, Davide Scaramuzza, Laurent Kneip:
A 5-Point Minimal Solver for Event Camera Relative Motion Estimation. ICCV 2023: 8015-8025 - [c16]Nikola Zubic, Daniel Gehrig, Mathias Gehrig, Davide Scaramuzza:
From Chaos Comes Order: Ordering Event Representations for Object Recognition and Detection. ICCV 2023: 12800-12810 - [c15]Benedek Forrai, Takahiro Miki, Daniel Gehrig, Marco Hutter, Davide Scaramuzza:
Event-based Agile Object Catching with a Quadrupedal Robot. ICRA 2023: 12177-12183 - [p1]Daniel Gehrig:
Effiziente, Datenbasierte Wahrnehmung mit Eventkameras. Ausgezeichnete Informatikdissertationen 2023: 121-130 - [i24]Asude Aydin, Mathias Gehrig, Daniel Gehrig, Davide Scaramuzza:
A Hybrid ANN-SNN Architecture for Low-Power and Low-Latency Visual Perception. CoRR abs/2303.14176 (2023) - [i23]Benedek Forrai, Takahiro Miki, Daniel Gehrig, Marco Hutter, Davide Scaramuzza:
Event-based Agile Object Catching with a Quadrupedal Robot. CoRR abs/2303.17479 (2023) - [i22]Nikola Zubic, Daniel Gehrig, Mathias Gehrig, Davide Scaramuzza:
From Chaos Comes Order: Ordering Event Representations for Object Detection. CoRR abs/2304.13455 (2023) - [i21]Mohammed Salah, Abdulla Ayyad, Muhammad Humais, Daniel Gehrig, Abdelqader Abusafieh, Lakmal D. Seneviratne, Davide Scaramuzza, Yahya H. Zweiri:
E-Calib: A Fast, Robust and Accurate Calibration Toolbox for Event Cameras. CoRR abs/2306.09078 (2023) - [i20]Roberto Pellerito, Marco Cannici, Daniel Gehrig, Joris Belhadj, Olivier Dubois-Matra, Massimo Casasco, Davide Scaramuzza:
End-to-End Learned Event- and Image-based Visual Odometry. CoRR abs/2309.09947 (2023) - [i19]Ling Gao, Hang Su, Daniel Gehrig, Marco Cannici, Davide Scaramuzza, Laurent Kneip:
A 5-Point Minimal Solver for Event Camera Relative Motion Estimation. CoRR abs/2309.17054 (2023) - 2022
- [j6]Nico Messikommer, Daniel Gehrig, Mathias Gehrig, Davide Scaramuzza:
Bridging the Gap Between Events and Frames Through Unsupervised Domain Adaptation. IEEE Robotics Autom. Lett. 7(2): 3515-3522 (2022) - [j5]Florian Mahlknecht, Daniel Gehrig, Jeremy Nash, Friedrich M. Rockenbauer, Benjamin Morrell, Jeff Delaune, Davide Scaramuzza:
Exploring Event Camera-Based Odometry for Planetary Robots. IEEE Robotics Autom. Lett. 7(4): 8651-8658 (2022) - [c14]Nico Messikommer, Stamatios Georgoulis, Daniel Gehrig, Stepan Tulyakov, Julius Erbach, Alfredo Bochicchio, Yuanyou Li, Davide Scaramuzza:
Multi-Bracket High Dynamic Range Imaging with Event Cameras. CVPR Workshops 2022: 546-556 - [c13]Simon Schaefer, Daniel Gehrig, Davide Scaramuzza:
AEGNN: Asynchronous Event-based Graph Neural Networks. CVPR 2022: 12361-12371 - [c12]Stepan Tulyakov, Alfredo Bochicchio, Daniel Gehrig, Stamatios Georgoulis, Yuanyou Li, Davide Scaramuzza:
Time Lens++: Event-based Frame Interpolation with Parametric Nonlinear Flow and Multi-scale Fusion. CVPR 2022: 17734-17743 - [c11]Zhaoning Sun, Nico Messikommer, Daniel Gehrig, Davide Scaramuzza:
ESS: Learning Event-Based Semantic Segmentation from Still Images. ECCV (34) 2022: 341-357 - [i18]Nico Messikommer, Stamatios Georgoulis, Daniel Gehrig, Stepan Tulyakov, Julius Erbach, Alfredo Bochicchio, Yuanyou Li, Davide Scaramuzza:
Multi-Bracket High Dynamic Range Imaging with Event Cameras. CoRR abs/2203.06622 (2022) - [i17]Zhaoning Sun, Nico Messikommer, Daniel Gehrig, Davide Scaramuzza:
ESS: Learning Event-based Semantic Segmentation from Still Images. CoRR abs/2203.10016 (2022) - [i16]Daniel Gehrig, Davide Scaramuzza:
Are High-Resolution Event Cameras Really Needed? CoRR abs/2203.14672 (2022) - [i15]Simon Schaefer, Daniel Gehrig, Davide Scaramuzza:
AEGNN: Asynchronous Event-based Graph Neural Networks. CoRR abs/2203.17149 (2022) - [i14]Stepan Tulyakov, Alfredo Bochicchio, Daniel Gehrig, Stamatios Georgoulis, Yuanyou Li, Davide Scaramuzza:
Time Lens++: Event-based Frame Interpolation with Parametric Non-linear Flow and Multi-scale Fusion. CoRR abs/2203.17191 (2022) - [i13]Florian Mahlknecht, Daniel Gehrig, Jeremy Nash, Friedrich M. Rockenbauer, Benjamin Morrell, Jeff Delaune, Davide Scaramuzza:
Exploring Event Camera-based Odometry for Planetary Robots. CoRR abs/2204.05880 (2022) - [i12]Daniel Gehrig, Davide Scaramuzza:
Pushing the Limits of Asynchronous Graph-based Object Detection with Event Cameras. CoRR abs/2211.12324 (2022) - 2021
- [j4]Daniel Gehrig, Michelle Rüegg, Mathias Gehrig, Javier Hidalgo-Carrió, Davide Scaramuzza:
Combining Events and Frames Using Recurrent Asynchronous Multimodal Networks for Monocular Depth Prediction. IEEE Robotics Autom. Lett. 6(2): 2822-2829 (2021) - [j3]Mathias Gehrig, Willem Aarents, Daniel Gehrig, Davide Scaramuzza:
DSEC: A Stereo Event Camera Dataset for Driving Scenarios. IEEE Robotics Autom. Lett. 6(3): 4947-4954 (2021) - [c10]Mathias Gehrig, Mario Millhäusler, Daniel Gehrig, Davide Scaramuzza:
E-RAFT: Dense Optical Flow from Event Cameras. 3DV 2021: 197-206 - [c9]Manasi Muglikar, Mathias Gehrig, Daniel Gehrig, Davide Scaramuzza:
How To Calibrate Your Event Camera. CVPR Workshops 2021: 1403-1409 - [c8]Stepan Tulyakov, Daniel Gehrig, Stamatios Georgoulis, Julius Erbach, Mathias Gehrig, Yuanyou Li, Davide Scaramuzza:
Time Lens: Event-Based Video Frame Interpolation. CVPR 2021: 16155-16164 - [i11]Daniel Gehrig, Michelle Rüegg, Mathias Gehrig, Javier Hidalgo-Carrió, Davide Scaramuzza:
Combining Events and Frames using Recurrent Asynchronous Multimodal Networks for Monocular Depth Prediction. CoRR abs/2102.09320 (2021) - [i10]Mathias Gehrig, Willem Aarents, Daniel Gehrig, Davide Scaramuzza:
DSEC: A Stereo Event Camera Dataset for Driving Scenarios. CoRR abs/2103.06011 (2021) - [i9]Manasi Muglikar, Mathias Gehrig, Daniel Gehrig, Davide Scaramuzza:
How to Calibrate Your Event Camera. CoRR abs/2105.12362 (2021) - [i8]Stepan Tulyakov, Daniel Gehrig, Stamatios Georgoulis, Julius Erbach, Mathias Gehrig, Yuanyou Li, Davide Scaramuzza:
TimeLens: Event-based Video Frame Interpolation. CoRR abs/2106.07286 (2021) - [i7]Mathias Gehrig, Mario Millhäusler, Daniel Gehrig, Davide Scaramuzza:
Dense Optical Flow from Event Cameras. CoRR abs/2108.10552 (2021) - [i6]Nico Messikommer, Daniel Gehrig, Mathias Gehrig, Davide Scaramuzza:
Bridging the Gap between Events and Frames through Unsupervised Domain Adaptation. CoRR abs/2109.02618 (2021) - 2020
- [j2]Daniel Gehrig, Henri Rebecq, Guillermo Gallego, Davide Scaramuzza:
EKLT: Asynchronous Photometric Feature Tracking Using Events and Frames. Int. J. Comput. Vis. 128(3): 601-618 (2020) - [j1]Daniel Gehrig, Henri Rebecq, Guillermo Gallego, Davide Scaramuzza:
Correction to: EKLT: Asynchronous Photometric Feature Tracking Using Events and Frames. Int. J. Comput. Vis. 128(3): 619 (2020) - [c7]Javier Hidalgo-Carrió, Daniel Gehrig, Davide Scaramuzza:
Learning Monocular Dense Depth from Events. 3DV 2020: 534-542 - [c6]Daniel Gehrig, Mathias Gehrig, Javier Hidalgo-Carrió, Davide Scaramuzza:
Video to Events: Recycling Video Datasets for Event Cameras. CVPR 2020: 3583-3592 - [c5]Nico Messikommer, Daniel Gehrig, Antonio Loquercio, Davide Scaramuzza:
Event-Based Asynchronous Sparse Convolutional Networks. ECCV (8) 2020: 415-431 - [c4]Cedric Scheerlinck, Henri Rebecq, Daniel Gehrig, Nick Barnes, Robert E. Mahony, Davide Scaramuzza:
Fast Image Reconstruction with an Event Camera. WACV 2020: 156-163 - [i5]Nico Messikommer, Daniel Gehrig, Antonio Loquercio, Davide Scaramuzza:
Event-based Asynchronous Sparse Convolutional Networks. CoRR abs/2003.09148 (2020) - [i4]Javier Hidalgo-Carrió, Daniel Gehrig, Davide Scaramuzza:
Learning Monocular Dense Depth from Events. CoRR abs/2010.08350 (2020)
2010 – 2019
- 2019
- [c3]Daniel Gehrig, Antonio Loquercio, Konstantinos G. Derpanis, Davide Scaramuzza:
End-to-End Learning of Representations for Asynchronous Event-Based Data. ICCV 2019: 5632-5642 - [i3]Daniel Gehrig, Antonio Loquercio, Konstantinos G. Derpanis, Davide Scaramuzza:
End-to-End Learning of Representations for Asynchronous Event-Based Data. CoRR abs/1904.08245 (2019) - [i2]Daniel Gehrig, Mathias Gehrig, Javier Hidalgo-Carrió, Davide Scaramuzza:
Video to Events: Bringing Modern Computer Vision Closer to Event Cameras. CoRR abs/1912.03095 (2019) - 2018
- [c2]Henri Rebecq, Daniel Gehrig, Davide Scaramuzza:
ESIM: an Open Event Camera Simulator. CoRL 2018: 969-982 - [c1]Daniel Gehrig, Henri Rebecq, Guillermo Gallego, Davide Scaramuzza:
Asynchronous, Photometric Feature Tracking Using Events and Frames. ECCV (12) 2018: 766-781 - [i1]Daniel Gehrig, Henri Rebecq, Guillermo Gallego, Davide Scaramuzza:
Asynchronous, Photometric Feature Tracking using Events and Frames. CoRR abs/1807.09713 (2018)
Coauthor Index
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last updated on 2024-10-11 17:28 CEST by the dblp team
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