12 citations · 26 across the 12 of their papers we have counts for
31 papers
Learning Neural Antiderivatives
Fizza Rubab, Ntumba Elie Nsampi, Martin Balint +4
Neural fields offer continuous, learnable representations that extend beyond traditional discrete formats in visual computing. We study the problem of learning neural representatio…
3inGAN: Learning a 3D Generative Model from Images of a Self-similar Scene
Animesh Karnewar, Oliver Wang, Tobias Ritschel +1
We introduce 3inGAN, an unconditional 3D generative model trained from 2D images of a single self-similar 3D scene. Such a model can be used to produce 3D "remixes" of a given scen…
OutCast: Outdoor Single-image Relighting with Cast Shadows
David Griffiths, Tobias Ritschel, Julien Philip
We propose a relighting method for outdoor images. Our method mainly focuses on predicting cast shadows in arbitrary novel lighting directions from a single image while also accoun…
Clean Implicit 3D Structure from Noisy 2D STEM Images
Hannah Kniesel, Timo Ropinski, Tim Bergner +5
Scanning Transmission Electron Microscopes (STEMs) acquire 2D images of a 3D sample on the scale of individual cell components. Unfortunately, these 2D images can be too noisy to b…
ONIX: an X-ray deep-learning tool for 3D reconstructions from sparse views
Yuhe Zhang, Zisheng Yao, Tobias Ritschel +1
Three-dimensional (3D) X-ray imaging techniques like tomography and confocal microscopy are crucial for academic and industrial applications. These approaches access 3D information…
Data-driven deep density estimation
Patrik Puchert, Pedro Hermosilla, Tobias Ritschel +1
Density estimation plays a crucial role in many data analysis tasks, as it infers a continuous probability density function (PDF) from discrete samples. Thus, it is used in tasks a…