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Daniel Gehrig
Daniel Gehrig
Ph.D. candidate, University of Zurich
Verified email at ifi.uzh.ch
Title
Cited by
Cited by
Year
ESIM: an open event camera simulator
H Rebecq, D Gehrig, D Scaramuzza
Conference on robot learning, 969-982, 2018
1752018
End-to-end learning of representations for asynchronous event-based data
D Gehrig, A Loquercio, KG Derpanis, D Scaramuzza
Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2019
1392019
Asynchronous, Photometric Feature Tracking using Events and Frames
D Gehrig
Robotics and Perception Group, University of Zurich, 2018
872018
Asynchronous, photometric feature tracking using events and frames
D Gehrig, H Rebecq, G Gallego, D Scaramuzza
Proceedings of the European Conference on Computer Vision (ECCV), 750-765, 2018
872018
Video to events: Recycling video datasets for event cameras
D Gehrig, M Gehrig, J Hidalgo-Carrió, D Scaramuzza
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020
752020
Fast image reconstruction with an event camera
C Scheerlinck, H Rebecq, D Gehrig, N Barnes, R Mahony, D Scaramuzza
Proceedings of the IEEE/CVF Winter Conference on Applications of Computer …, 2020
742020
EKLT: Asynchronous photometric feature tracking using events and frames
D Gehrig, H Rebecq, G Gallego, D Scaramuzza
International Journal of Computer Vision 128 (3), 601-618, 2020
682020
Event-based Asynchronous Sparse Convolutional Networks
N Messikommer*, D Gehrig*, A Loquercio, D Scaramuzza
Proceedings of the European Conference on Computer Vision (ECCV), 2020
472020
Dsec: A stereo event camera dataset for driving scenarios
M Gehrig, W Aarents, D Gehrig, D Scaramuzza
IEEE Robotics and Automation Letters 6 (3), 4947-4954, 2021
412021
Learning Monocular Dense Depth from Events
J Hidalgo-Carrió, D Gehrig, D Scaramuzza
International Conference on 3D Vision (3DV), 2020
342020
Combining events and frames using recurrent asynchronous multimodal networks for monocular depth prediction
D Gehrig, M Rüegg, M Gehrig, J Hidalgo-Carrió, D Scaramuzza
IEEE Robotics and Automation Letters 6 (2), 2822-2829, 2021
292021
TimeLens: Event-based Video Frame Interpolation
S Tulyakov*, D Gehrig*, S Georgoulis, J Erbach, M Gehrig, Y Li, ...
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021
262021
E-raft: Dense optical flow from event cameras
M Gehrig, M Millhäusler, D Gehrig, D Scaramuzza
2021 International Conference on 3D Vision (3DV), 197-206, 2021
12*2021
How to calibrate your event camera
M Muglikar, M Gehrig, D Gehrig, D Scaramuzza
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021
62021
Time Lens++: Event-based Frame Interpolation with Parametric Non-linear Flow and Multi-scale Fusion
S Tulyakov, A Bochicchio, D Gehrig, S Georgoulis, Y Li, D Scaramuzza
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022
22022
Are High-Resolution Event Cameras Really Needed?
D Gehrig, D Scaramuzza
arXiv preprint arXiv:2203.14672, 2022
12022
Bridging the Gap between Events and Frames through Unsupervised Domain Adaptation
N Messikommer, D Gehrig, M Gehrig, D Scaramuzza
IEEE Robotics and Automation Letters 7 (2), 3515-3522, 2022
12022
AEGNN: Asynchronous Event-based Graph Neural Networks
S Schaefer, D Gehrig, D Scaramuzza
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022
12022
Exploring Event Camera-based Odometry for Planetary Robots
F Mahlknecht, D Gehrig, J Nash, FM Rockenbauer, B Morrell, J Delaune, ...
arXiv preprint arXiv:2204.05880, 2022
2022
ESS: Learning Event-based Semantic Segmentation from Still Images
Z Sun, N Messikommer, D Gehrig, D Scaramuzza
arXiv preprint arXiv:2203.10016, 2022
2022
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Articles 1–20