10 citations · 15 across the 9 of their papers we have counts for
5 papers · 1 filter
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…
The Probabilistic Normal Epipolar Constraint for Frame-To-Frame Rotation Optimization under Uncertain Feature Positions
Dominik Muhle, Lukas Koestler, Nikolaus Demmel +2
The estimation of the relative pose of two camera views is a fundamental problem in computer vision. Kneip et al. proposed to solve this problem by introducing the normal epipolar…
Neural Implicit Representations for Physical Parameter Inference from a Single Video
Florian Hofherr, Lukas Koestler, Florian Bernard +1
Neural networks have recently been used to analyze diverse physical systems and to identify the underlying dynamics. While existing methods achieve impressive results, they are lim…
Intrinsic Neural Fields: Learning Functions on Manifolds
Lukas Koestler, Daniel Grittner, Michael Moeller +2
Neural fields have gained significant attention in the computer vision community due to their excellent performance in novel view synthesis, geometry reconstruction, and generative…