4 citations · 8 across the 5 of their papers we have counts for
6 papers
ControlEvents: Controllable Synthesis of Event Camera Datawith Foundational Prior from Image Diffusion Models
Yixuan Hu, Yuxuan Xue, Simon Klenk +2
In recent years, event cameras have gained significant attention due to their bio-inspired properties, such as high temporal resolution and high dynamic range. However, obtaining l…
Deep Event Visual Odometry
Simon Klenk, Marvin Motzet, Lukas Koestler +1
Event cameras offer the exciting possibility of tracking the camera's pose during high-speed motion and in adverse lighting conditions. Despite this promise, existing event-based m…
Masked Event Modeling: Self-Supervised Pretraining for Event Cameras
Simon Klenk, David Bonello, Lukas Koestler +2
Event cameras asynchronously capture brightness changes with low latency, high temporal resolution, and high dynamic range. However, annotation of event data is a costly and labori…
E-NeRF: Neural Radiance Fields from a Moving Event Camera
Simon Klenk, Lukas Koestler, Davide Scaramuzza +1
Estimating neural radiance fields (NeRFs) from "ideal" images has been extensively studied in the computer vision community. Most approaches assume optimal illumination and slow ca…
TUM-VIE: The TUM Stereo Visual-Inertial Event Dataset
Simon Klenk, Jason Chui, Nikolaus Demmel +1
Event cameras are bio-inspired vision sensors which measure per pixel brightness changes. They offer numerous benefits over traditional, frame-based cameras, including low latency,…
Event-Based Feature Tracking in Continuous Time with Sliding Window Optimization
Jason Chui, Simon Klenk, Daniel Cremers
We propose a novel method for continuous-time feature tracking in event cameras. To this end, we track features by aligning events along an estimated trajectory in space-time such…