8 citations · 8 across the 1 of their papers we have counts for
6 papers · 1 filter
Data-Driven Feature Tracking for Event Cameras With and Without Frames
Nico Messikommer, Carter Fang, Mathias Gehrig +2
Because of their high temporal resolution, increased resilience to motion blur, and very sparse output, event cameras have been shown to be ideal for low-latency and low-bandwidth…
Reinforcement Learning Meets Visual Odometry
Nico Messikommer, Giovanni Cioffi, Mathias Gehrig +1
Visual Odometry (VO) is essential to downstream mobile robotics and augmented/virtual reality tasks. Despite recent advances, existing VO methods still rely on heuristic design cho…
State Space Models for Event Cameras
Nikola ZubiÄ, Mathias Gehrig, Davide Scaramuzza
Today, state-of-the-art deep neural networks that process event-camera data first convert a temporal window of events into dense, grid-like input representations. As such, they exh…
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…
LEOD: Label-Efficient Object Detection for Event Cameras
Ziyi Wu, Mathias Gehrig, Qing Lyu +2
Object detection with event cameras benefits from the sensor's low latency and high dynamic range. However, it is costly to fully label event streams for supervised training due to…
Dense Continuous-Time Optical Flow from Events and Frames
Mathias Gehrig, Manasi Muglikar, Davide Scaramuzza
We present a method for estimating dense continuous-time optical flow from event data. Traditional dense optical flow methods compute the pixel displacement between two images. Due…