Daniel Gehrig

Daniel Gehrig

Universität Zürich

H-index: 18

Europe-Switzerland

About Daniel Gehrig

Daniel Gehrig, With an exceptional h-index of 18 and a recent h-index of 18 (since 2020), a distinguished researcher at Universität Zürich, specializes in the field of Computer Vision, Deep Learning, Event Cameras, Robotics.

His recent articles reflect a diverse array of research interests and contributions to the field:

An N-Point Linear Solver for Line and Motion Estimation with Event Cameras

End-to-End Learned Event-and Image-based Visual Odometry

E-Calib: A Fast, Robust and Accurate Calibration Toolbox for Event Cameras

Event-based Agile Object Catching with a Quadrupedal Robot

A Hybrid ANN-SNN Architecture for Low-Power and Low-Latency Visual Perception

A 5-Point Minimal Solver for Event Camera Relative Motion Estimation

From Chaos Comes Order: Ordering Event Representations for Object Recognition and Detection

Pushing the limits of asynchronous graph-based object detection with event cameras

Daniel Gehrig Information

University

Position

Ph.D. candidate

Citations(all)

2107

Citations(since 2020)

2101

Cited By

271

hIndex(all)

18

hIndex(since 2020)

18

i10Index(all)

19

i10Index(since 2020)

19

Email

University Profile Page

Google Scholar

Daniel Gehrig Skills & Research Interests

Computer Vision

Deep Learning

Event Cameras

Robotics

Top articles of Daniel Gehrig

An N-Point Linear Solver for Line and Motion Estimation with Event Cameras

arXiv preprint arXiv:2404.00842

2024/4/1

End-to-End Learned Event-and Image-based Visual Odometry

arXiv preprint arXiv:2309.09947

2023/9/18

E-Calib: A Fast, Robust and Accurate Calibration Toolbox for Event Cameras

arXiv preprint arXiv:2306.09078

2023/6/15

Event-based Agile Object Catching with a Quadrupedal Robot

2023/3/30

A Hybrid ANN-SNN Architecture for Low-Power and Low-Latency Visual Perception

arXiv preprint arXiv:2303.14176

2023/3/24

A 5-Point Minimal Solver for Event Camera Relative Motion Estimation

2023

From Chaos Comes Order: Ordering Event Representations for Object Recognition and Detection

2023/4/26

Pushing the limits of asynchronous graph-based object detection with event cameras

arXiv preprint arXiv:2211.12324

2022/11/22

Daniel Gehrig
Daniel Gehrig

H-Index: 7

Davide Scaramuzza
Davide Scaramuzza

H-Index: 66

ESS: Learning Event-based Semantic Segmentation from Still Images

2022/8/1

Exploring Event Camera-based Odometry for Planetary Robots

IEEE Robotics and Automation Letters

2022/4/12

Daniel Gehrig
Daniel Gehrig

H-Index: 7

Davide Scaramuzza
Davide Scaramuzza

H-Index: 66

Are high-resolution event cameras really needed?

arXiv preprint: arXiv:2203.14672

2022/3/29

Daniel Gehrig
Daniel Gehrig

H-Index: 7

Davide Scaramuzza
Davide Scaramuzza

H-Index: 66

Bridging the Gap between Events and Frames through Unsupervised Domain Adaptation

IEEE Robotics and Automation Letters

2022/1/25

Time Lens++: Event-based Frame Interpolation with Parametric Non-linear Flow and Multi-scale Fusion

2022/4/15

Daniel Gehrig
Daniel Gehrig

H-Index: 7

Davide Scaramuzza
Davide Scaramuzza

H-Index: 66

AEGNN: Asynchronous Event-based Graph Neural Networks

2022/4/15

Daniel Gehrig
Daniel Gehrig

H-Index: 7

Davide Scaramuzza
Davide Scaramuzza

H-Index: 66

Multi-Bracket High Dynamic Range Imaging with Event Cameras

2022/4/15

E-RAFT: Dense Optical Flow from Event Cameras

2021/11/1

DSEC: A Stereo Event Camera Dataset for Driving Scenarios

IEEE Robotics and Automation Letters

2021/3/25

Combining Events and Frames using Recurrent Asynchronous Multimodal Networks for Monocular Depth Prediction

IEEE Robotics and Automation Letters

2021/2/19

How to Calibrate your Event Camera

2021/4/27

Learning Monocular Dense Depth from Events

2020/10/16

See List of Professors in Daniel Gehrig University(Universität Zürich)

Co-Authors

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