Publications (32)
Neural Inertial Odometry from Lie Events
Royina Karegoudra Jayanth, Yinshuang Xu, Evangelos Chatzipantazis +2
Neural displacement priors (NDP) can reduce the drift in inertial odometry and provide uncertainty estimates that can be readily fused with off-the-shelf filters. However, they fai…
TimeLens: Event-based Video Frame Interpolation
Stepan Tulyakov, Daniel Gehrig, Stamatios Georgoulis +4
State-of-the-art frame interpolation methods generate intermediate frames by inferring object motions in the image from consecutive key-frames. In the absence of additional informa…
Multi-Bracket High Dynamic Range Imaging with Event Cameras
Nico Messikommer, Stamatios Georgoulis, Daniel Gehrig +5
Modern high dynamic range (HDR) imaging pipelines align and fuse multiple low dynamic range (LDR) images captured at different exposure times. While these methods work well in stat…
Neural Events: Discrete Asynchronous Autoencoders for Event-Based Vision
Roberto Pellerito, Daniel Gehrig, Shintaro Shiba +1
Event cameras capture dynamic scenes with exceptional temporal fidelity by representing them as a continuous stream of microsecond resolution \textit{events}. Each individual event…
End-to-End Learning of Representations for Asynchronous Event-Based Data
Daniel Gehrig, Antonio Loquercio, Konstantinos G. Derpanis +1
Event cameras are vision sensors that record asynchronous streams of per-pixel brightness changes, referred to as "events". They have appealing advantages over frame-based cameras…
ESS: Learning Event-based Semantic Segmentation from Still Images
Zhaoning Sun, Nico Messikommer, Daniel Gehrig +1
Retrieving accurate semantic information in challenging high dynamic range (HDR) and high-speed conditions remains an open challenge for image-based algorithms due to severe image…
ETAP: Event-based Tracking of Any Point
Friedhelm Hamann, Daniel Gehrig, Filbert Febryanto +2
Tracking any point (TAP) recently shifted the motion estimation paradigm from focusing on individual salient points with local templates to tracking arbitrary points with global im…
DSEC: A Stereo Event Camera Dataset for Driving Scenarios
Mathias Gehrig, Willem Aarents, Daniel Gehrig +1
Once an academic venture, autonomous driving has received unparalleled corporate funding in the last decade. Still, the operating conditions of current autonomous cars are mostly r…
Time Lens++: Event-based Frame Interpolation with Parametric Non-linear Flow and Multi-scale Fusion
Stepan Tulyakov, Alfredo Bochicchio, Daniel Gehrig +3
Recently, video frame interpolation using a combination of frame- and event-based cameras has surpassed traditional image-based methods both in terms of performance and memory effi…
A Hybrid ANN-SNN Architecture for Low-Power and Low-Latency Visual Perception
Asude Aydin, Mathias Gehrig, Daniel Gehrig +1
Spiking Neural Networks (SNN) are a class of bio-inspired neural networks that promise to bring low-power and low-latency inference to edge devices through asynchronous and sparse…
Pushing the Limits of Asynchronous Graph-based Object Detection with Event Cameras
Daniel Gehrig, Davide Scaramuzza
State-of-the-art machine-learning methods for event cameras treat events as dense representations and process them with conventional deep neural networks. Thus, they fail to mainta…
Combining Events and Frames using Recurrent Asynchronous Multimodal Networks for Monocular Depth Prediction
Daniel Gehrig, Michelle Rüegg, Mathias Gehrig +2
Event cameras are novel vision sensors that report per-pixel brightness changes as a stream of asynchronous "events". They offer significant advantages compared to standard cameras…
Learning Monocular Dense Depth from Events
Javier Hidalgo-Carrió, Daniel Gehrig, Davide Scaramuzza
Event cameras are novel sensors that output brightness changes in the form of a stream of asynchronous events instead of intensity frames. Compared to conventional image sensors, t…
A Unified Framework for Event-based Frame Interpolation with Ad-hoc Deblurring in the Wild
Lei Sun, Daniel Gehrig, Christos Sakaridis +7
Effective video frame interpolation hinges on the adept handling of motion in the input scene. Prior work acknowledges asynchronous event information for this, but often overlooks…
Video to Events: Recycling Video Datasets for Event Cameras
Daniel Gehrig, Mathias Gehrig, Javier Hidalgo-Carrió +1
Event cameras are novel sensors that output brightness changes in the form of a stream of asynchronous "events" instead of intensity frames. They offer significant advantages with…
E-Calib: A Fast, Robust and Accurate Calibration Toolbox for Event Cameras
Mohammed Salah, Abdulla Ayyad, Muhammad Humais +5
Event cameras triggered a paradigm shift in the computer vision community delineated by their asynchronous nature, low latency, and high dynamic range. Calibration of event cameras…
Event-based Asynchronous Sparse Convolutional Networks
Nico Messikommer, Daniel Gehrig, Antonio Loquercio +1
Event cameras are bio-inspired sensors that respond to per-pixel brightness changes in the form of asynchronous and sparse "events". Recently, pattern recognition algorithms, such…
A 5-Point Minimal Solver for Event Camera Relative Motion Estimation
Ling Gao, Hang Su, Daniel Gehrig +3
Event-based cameras are ideal for line-based motion estimation, since they predominantly respond to edges in the scene. However, accurately determining the camera displacement base…
RoSHI: A Versatile Robot-oriented Suit for Human Data In-the-Wild
Wenjing Margaret Mao, Jefferson Ng, Luyang Hu +2
Scaling up robot learning will likely require human data containing rich and long-horizon interactions in the wild. Existing approaches for collecting such data trade off portabili…
A Linear N-Point Solver for Structure and Motion from Asynchronous Tracks
Hang Su, Yunlong Feng, Daniel Gehrig +4
Structure and continuous motion estimation from point correspondences is a fundamental problem in computer vision that has been powered by well-known algorithms such as the familia…
AEGNN: Asynchronous Event-based Graph Neural Networks
Simon Schaefer, Daniel Gehrig, Davide Scaramuzza
The best performing learning algorithms devised for event cameras work by first converting events into dense representations that are then processed using standard CNNs. However, t…
Bridging the Gap between Events and Frames through Unsupervised Domain Adaptation
Nico Messikommer, Daniel Gehrig, Mathias Gehrig +1
Reliable perception during fast motion maneuvers or in high dynamic range environments is crucial for robotic systems. Since event cameras are robust to these challenging condition…
Exploring Event Camera-based Odometry for Planetary Robots
Florian Mahlknecht, Daniel Gehrig, Jeremy Nash +4
Due to their resilience to motion blur and high robustness in low-light and high dynamic range conditions, event cameras are poised to become enabling sensors for vision-based expl…
How to Calibrate Your Event Camera
Manasi Muglikar, Mathias Gehrig, Daniel Gehrig +1
We propose a generic event camera calibration framework using image reconstruction. Instead of relying on blinking LED patterns or external screens, we show that neural-network-bas…
An N-Point Linear Solver for Line and Motion Estimation with Event Cameras
Ling Gao, Daniel Gehrig, Hang Su +2
Event cameras respond primarily to edges--formed by strong gradients--and are thus particularly well-suited for line-based motion estimation. Recent work has shown that events gene…
Are High-Resolution Event Cameras Really Needed?
Daniel Gehrig, Davide Scaramuzza
Due to their outstanding properties in challenging conditions, event cameras have become indispensable in a wide range of applications, ranging from automotive, computational photo…
Asynchronous, Photometric Feature Tracking using Events and Frames
Daniel Gehrig, Henri Rebecq, Guillermo Gallego +1
We present a method that leverages the complementarity of event cameras and standard cameras to track visual features with low-latency. Event cameras are novel sensors that output…
EqNIO: Subequivariant Neural Inertial Odometry
Royina Karegoudra Jayanth, Yinshuang Xu, Ziyun Wang +3
Neural networks are seeing rapid adoption in purely inertial odometry, where accelerometer and gyroscope measurements from commodity inertial measurement units (IMU) are used to re…
Deep Visual Odometry with Events and Frames
Roberto Pellerito, Marco Cannici, Daniel Gehrig +4
Visual Odometry (VO) is crucial for autonomous robotic navigation, especially in GPS-denied environments like planetary terrains. To improve robustness, recent model-based VO syste…
Event-based Agile Object Catching with a Quadrupedal Robot
Benedek Forrai, Takahiro Miki, Daniel Gehrig +2
Quadrupedal robots are conquering various indoor and outdoor applications due to their ability to navigate challenging uneven terrains. Exteroceptive information greatly enhances t…
E-RAFT: Dense Optical Flow from Event Cameras
Mathias Gehrig, Mario Millhäusler, Daniel Gehrig +1
We propose to incorporate feature correlation and sequential processing into dense optical flow estimation from event cameras. Modern frame-based optical flow methods heavily rely…
From Chaos Comes Order: Ordering Event Representations for Object Recognition and Detection
Nikola ZubiÄ, Daniel Gehrig, Mathias Gehrig +1
Today, state-of-the-art deep neural networks that process events first convert them into dense, grid-like input representations before using an off-the-shelf network. However, sele…