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Daniel Gehrig
Daniel Gehrig
Postdoctoral researcher, GRASP Lab, University of Pennsylvania
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Zitiert von
Zitiert von
Jahr
ESIM: An Open Event Camera Simulator
H Rebecq, D Gehrig, D Scaramuzza
Conference on Robot Learning, 969-982, 2018
4042018
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
2942019
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
2142021
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
1942020
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
1842020
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
1642020
Time Lens: 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
1592021
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
1512018
Event-based Asynchronous Sparse Convolutional Networks
N Messikommer*, D Gehrig*, A Loquercio, D Scaramuzza
Proceedings of the European Conference on Computer Vision (ECCV), 2020
1362020
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
1082021
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
1052021
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
962022
Learning Monocular Dense Depth from Events
J Hidalgo-Carrió, D Gehrig, D Scaramuzza
International Conference on 3D Vision (3DV), 2020
922020
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
892022
ESS: Learning Event-based Semantic Segmentation from Still Images
Z Sun, N Messikommer, D Gehrig, D Scaramuzza
Computer Vision–ECCV 2022: 17th European Conference, Tel Aviv, Israel …, 2022
612022
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
502021
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
442022
Exploring Event Camera-based Odometry for Planetary Robots
F Mahlknecht, D Gehrig, J Nash, FM Rockenbauer, B Morrell, J Delaune, ...
IEEE Robotics and Automation Letters, 2022
282022
Multi-Bracket High Dynamic Range Imaging with Event Cameras
N Messikommer, S Georgoulis, D Gehrig, S Tulyakov, J Erbach, ...
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022
282022
Pushing the limits of asynchronous graph-based object detection with event cameras
D Gehrig, D Scaramuzza
arXiv preprint arXiv:2211.12324, 2022
162022
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