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
1152018
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
902019
Asynchronous, Photometric Feature Tracking using Events and Frames
D Gehrig
Robotics and Perception Group, University of Zurich, 2018
702018
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
702018
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
442020
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
432020
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
38*2020
Event-based Asynchronous Sparse Convolutional Networks
N Messikommer*, D Gehrig*, A Loquercio, D Scaramuzza
Proceedings of the European Conference on Computer Vision (ECCV), 2020
262020
Learning Monocular Dense Depth from Events
J Hidalgo-Carrió, D Gehrig, D Scaramuzza
International Conference on 3D Vision (3DV), 2020
152020
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
112021
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
42021
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
22021
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
22021
Bridging the Gap between Events and Frames through Unsupervised Domain Adaptation
N Messikommer, D Gehrig, M Gehrig, D Scaramuzza
arXiv preprint arXiv:2109.02618, 2021
2021
Dense Optical Flow from Event Cameras
M Gehrig, M Millhäusler, D Gehrig, D Scaramuzza
arXiv preprint arXiv:2108.10552, 2021
2021
Event-based Monocular Depth Prediction in Night Driving
J Hidalgo-Carrió, D Gehrig, D Scaramuzza
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Articles 1–16