204 citations · 703 across the 41 of their papers we have counts for
51 papers
Volumetric Inverse Rendering via Neural Radiative Transfer
Ntumba Elie Nsampi, Adarsh Djeacoumar, Hans-Peter Seidel +2
Volumetric inverse rendering seeks to recover the optical properties of participating media from images. Existing approaches either rely on differentiable stochastic light transpor…
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…
Bracket Diffusion: HDR Image Generation by Consistent LDR Denoising
Mojtaba Bemana, Thomas Leimkühler, Karol Myszkowski +2
We demonstrate generating HDR images using the concerted action of multiple black-box, pre-trained LDR image diffusion models. Relying on a pre-trained LDR generative diffusion mod…
Patternshop: Editing Point Patterns by Image Manipulation
Xingchang Huang, Tobias Ritschel, Hans-Peter Seidel +2
Point patterns are characterized by their density and correlation. While spatial variation of density is well-understood, analysis and synthesis of spatially-varying correlation is…
Zero Grads: Learning Local Surrogate Losses for Non-Differentiable Graphics
Michael Fischer, Tobias Ritschel
Gradient-based optimization is now ubiquitous across graphics, but unfortunately can not be applied to problems with undefined or zero gradients. To circumvent this issue, the loss…
Megahertz X-ray Multi-projection imaging
Pablo Villanueva-Perez, Valerio Bellucci, Yuhe Zhang +21
X-ray time-resolved tomography is one of the most popular X-ray techniques to probe dynamics in three dimensions (3D). Recent developments in time-resolved tomography opened the po…
Neural Field Convolutions by Repeated Differentiation
Ntumba Elie Nsampi, Adarsh Djeacoumar, Hans-Peter Seidel +2
Neural fields are evolving towards a general-purpose continuous representation for visual computing. Yet, despite their numerous appealing properties, they are hardly amenable to s…
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…
Plateau-reduced Differentiable Path Tracing
Michael Fischer, Tobias Ritschel
Current differentiable renderers provide light transport gradients with respect to arbitrary scene parameters. However, the mere existence of these gradients does not guarantee use…
Learning to Rasterize Differentiably
Chenghao Wu, Hamila Mailee, Zahra Montazeri +1
Differentiable rasterization changes the standard formulation of primitive rasterization -- by enabling gradient flow from a pixel to its underlying triangles -- using distribution…
Learning to Learn and Sample BRDFs
Chen Liu, Michael Fischer, Tobias Ritschel
We propose a method to accelerate the joint process of physically acquiring and learning neural Bi-directional Reflectance Distribution Function (BRDF) models. While BRDF learning…
ReLU Fields: The Little Non-linearity That Could
Animesh Karnewar, Tobias Ritschel, Oliver Wang +1
In many recent works, multi-layer perceptions (MLPs) have been shown to be suitable for modeling complex spatially-varying functions including images and 3D scenes. Although the ML…
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…
Metappearance: Meta-Learning for Visual Appearance Reproduction
Michael Fischer, Tobias Ritschel
There currently exist two main approaches to reproducing visual appearance using Machine Learning (ML): The first is training models that generalize over different instances of a p…
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…
Eikonal Fields for Refractive Novel-View Synthesis
Mojtaba Bemana, Karol Myszkowski, Jeppe Revall Frisvad +2
We tackle the problem of generating novel-view images from collections of 2D images showing refractive and reflective objects. Current solutions assume opaque or transparent light…
Variance-Aware Weight Initialization for Point Convolutional Neural Networks
Pedro Hermosilla, Michael Schelling, Tobias Ritschel +1
Appropriate weight initialization has been of key importance to successfully train neural networks. Recently, batch normalization has diminished the role of weight initialization b…
Metameric Varifocal Holography
David R. Walton, Koray Kavaklı, Rafael Kuffner dos Anjos +6
Computer-Generated Holography (CGH) offers the potential for genuine, high-quality three-dimensional visuals. However, fulfilling this potential remains a practical challenge due t…
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…
Unsupervised Learning of 3D Object Categories from Videos in the Wild
Philipp Henzler, Jeremy Reizenstein, Patrick Labatut +4
Our goal is to learn a deep network that, given a small number of images of an object of a given category, reconstructs it in 3D. While several recent works have obtained analogous…
Blue Noise Plots
Christian van Onzenoodt, Gurprit Singh, Timo Ropinski +1
We propose Blue Noise Plots, two-dimensional dot plots that depict data points of univariate data sets. While often one-dimensional strip plots are used to depict such data, one of…
Generative Modelling of BRDF Textures from Flash Images
Philipp Henzler, Valentin Deschaintre, Niloy J. Mitra +1
We learn a latent space for easy capture, consistent interpolation, and efficient reproduction of visual material appearance. When users provide a photo of a stationary natural mat…
Neural BRDF Representation and Importance Sampling
Alejandro Sztrajman, Gilles Rainer, Tobias Ritschel +1
Controlled capture of real-world material appearance yields tabulated sets of highly realistic reflectance data. In practice, however, its high memory footprint requires compressin…