no code implementations • 13 Feb 2024 • Michael Fischer, Zhengqin Li, Thu Nguyen-Phuoc, Aljaz Bozic, Zhao Dong, Carl Marshall, Tobias Ritschel
A Neural Radiance Field (NeRF) encodes the specific relation of 3D geometry and appearance of a scene.
no code implementations • 17 Jan 2024 • Yuhe Zhang, Zisheng Yao, Robert Klöfkorn, Tobias Ritschel, Pablo Villanueva-Perez
The X-ray flux provided by X-ray free-electron lasers and storage rings offers new spatiotemporal possibilities to study in-situ and operando dynamics, even using single pulses of such facilities.
no code implementations • 10 Oct 2023 • Wenxin Liu, Michael Fischer, Paul D. Yoo, Tobias Ritschel
Bounding volumes are an established concept in computer graphics and vision tasks but have seen little change since their early inception.
no code implementations • 10 Aug 2023 • 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.
no code implementations • 19 May 2023 • Pablo Villanueva-Perez, Valerio Bellucci, Yuhe Zhang, Sarlota Birnsteinova, Rita Graceffa, Luigi Adriano, Eleni Myrto Asimakopoulou, Ilia Petrov, Zisheng Yao, Marco Romagnoni, Andrea Mazzolari, Romain Letrun, Chan Kim, Jayanath C. P. Koliyadu, Carsten Deiter, Richard Bean, Gabriele Giovanetti, Luca Gelisio, Tobias Ritschel, Adrian Mancuso, Henry N. Chapman, Alke Meents, Tokushi Sato, Patrik Vagovic
X-ray time-resolved tomography is one of the most popular X-ray techniques to probe dynamics in three dimensions (3D).
no code implementations • 4 Apr 2023 • Ntumba Elie Nsampi, Adarsh Djeacoumar, Hans-Peter Seidel, Tobias Ritschel, Thomas Leimkühler
Neural fields are evolving towards a general-purpose continuous representation for visual computing.
no code implementations • CVPR 2023 • Michael Fischer, Tobias Ritschel
Current differentiable renderers provide light transport gradients with respect to arbitrary scene parameters.
no code implementations • 27 Nov 2022 • Animesh Karnewar, Oliver Wang, Tobias Ritschel, Niloy Mitra
We introduce 3inGAN, an unconditional 3D generative model trained from 2D images of a single self-similar 3D scene.
no code implementations • 23 Nov 2022 • Chenghao Wu, Zahra Montazeri, Tobias Ritschel
Differentiable rasterization changes the common formulation of primitive rasterization -- which has zero gradients almost everywhere, due to discontinuous edges and occlusion -- to an alternative one, which is not subject to this limitation and has similar optima.
1 code implementation • 7 Oct 2022 • 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.
no code implementations • 22 May 2022 • Animesh Karnewar, Tobias Ritschel, Oliver Wang, Niloy J. Mitra
Although the MLPs are able to represent complex scenes with unprecedented quality and memory footprint, this expressive power of the MLPs, however, comes at the cost of long training and inference times.
no code implementations • 20 Apr 2022 • David Griffiths, Tobias Ritschel, Julien Philip
We propose a relighting method for outdoor images.
1 code implementation • CVPR 2022 • Hannah Kniesel, Timo Ropinski, Tim Bergner, Kavitha Shaga Devan, Clarissa Read, Paul Walther, Tobias Ritschel, Pedro Hermosilla
Scanning Transmission Electron Microscopes (STEMs) acquire 2D images of a 3D sample on the scale of individual cell components.
no code implementations • 1 Mar 2022 • Yuhe Zhang, Zisheng Yao, Tobias Ritschel, Pablo Villanueva-Perez
We anticipate that ONIX will become a crucial tool for the X-ray community by i) enabling the study of fast dynamics not possible today when implemented together with X-ray multi-projection imaging, and ii) enhancing the volumetric information and capabilities of X-ray stereoscopic imaging in medical applications.
no code implementations • 2 Feb 2022 • Mojtaba Bemana, Karol Myszkowski, Jeppe Revall Frisvad, Hans-Peter Seidel, Tobias Ritschel
We tackle the problem of generating novel-view images from collections of 2D images showing refractive and reflective objects.
no code implementations • 7 Dec 2021 • Pedro Hermosilla, Michael Schelling, Tobias Ritschel, Timo Ropinski
Appropriate weight initialization has been of key importance to successfully train neural networks.
1 code implementation • 23 Jul 2021 • Patrik Puchert, Pedro Hermosilla, Tobias Ritschel, Timo Ropinski
Density estimation plays a crucial role in many data analysis tasks, as it infers a continuous probability density function (PDF) from discrete samples.
no code implementations • CVPR 2021 • Philipp Henzler, Jeremy Reizenstein, Patrick Labatut, Roman Shapovalov, Tobias Ritschel, Andrea Vedaldi, David Novotny
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.
no code implementations • 23 Feb 2021 • Philipp Henzler, Valentin Deschaintre, Niloy J. Mitra, Tobias Ritschel
We learn a latent space for easy capture, consistent interpolation, and efficient reproduction of visual material appearance.
no code implementations • 11 Feb 2021 • Alejandro Sztrajman, Gilles Rainer, Tobias Ritschel, Tim Weyrich
Additionally, we propose a novel approach to make our representation amenable to importance sampling: rather than inverting the trained networks, we learn to encode them in a more compact embedding that can be mapped to parameters of an analytic BRDF for which importance sampling is known.
no code implementations • 22 Dec 2020 • Uğur Çoğalan, Mojtaba Bemana, Karol Myszkowski, Hans-Peter Seidel, Tobias Ritschel
Regrettably, capturing DISTORTED sensor readings is time-consuming; as well, there is a lack of CLEAN HDR videos.
no code implementations • 14 Dec 2020 • Andrey Polyakov, Katayoon Mohseni, Roberto Felici, Christian Tusche, Ying-Jiun Chen, Vitaliy Feyer, Jochen Geck, Tobias Ritschel, Juan Rubio-Zuazo, German R. Castro, Holger L. Meyerheim, Stuart S. P. Parkin
Spin-momentum locking in topological insulators and materials with Rashba-type interactions is an extremely attractive feature for novel spintronic devices and is therefore under intense investigation.
Mesoscale and Nanoscale Physics
no code implementations • 2 Dec 2020 • David Griffiths, Jan Boehm, Tobias Ritschel
This can be overcome by a novel form of training, where an additional network is employed to steer the optimization itself to explore the entire parameter space i. e., to be curious, and hence, to resolve those ambiguities and find workable minima.
no code implementations • 5 Oct 2020 • Connor Daly, Yuzuko Nakamura, Tobias Ritschel
The motion of picking up and placing an object in 3D space is full of subtle detail.
no code implementations • 1 Oct 2020 • Mojtaba Bemana, Karol Myszkowski, Hans-Peter Seidel, Tobias Ritschel
We suggest to represent an X-Field -a set of 2D images taken across different view, time or illumination conditions, i. e., video, light field, reflectance fields or combinations thereof-by learning a neural network (NN) to map their view, time or light coordinates to 2D images.
1 code implementation • ICLR 2021 • Pedro Hermosilla, Marco Schäfer, Matěj Lang, Gloria Fackelmann, Pere Pau Vázquez, Barbora Kozlíková, Michael Krone, Tobias Ritschel, Timo Ropinski
Proteins perform a large variety of functions in living organisms, thus playing a key role in biology.
1 code implementation • ECCV 2020 • David Griffiths, Jan Boehm, Tobias Ritschel
As we assume the scene not to be labeled by centers, no classic loss, such as Chamfer can be used to train it.
1 code implementation • CVPR 2020 • Philipp Henzler, Niloy J. Mitra, Tobias Ritschel
We propose a generative model of 2D and 3D natural textures with diversity, visual fidelity and at high computational efficiency.
no code implementations • 30 Oct 2019 • Mojtaba Bemana, Karol Myszkowski, Hans-Peter Seidel, Tobias Ritschel
We suggest representing light field (LF) videos as "one-off" neural networks (NN), i. e., a learned mapping from view-plus-time coordinates to high-resolution color values, trained on sparse views.
1 code implementation • ICCV 2019 • Pedro Hermosilla, Tobias Ritschel, Timo Ropinski
We show that denoising of 3D point clouds can be learned unsupervised, directly from noisy 3D point cloud data only.
no code implementations • ICCV 2019 • Philipp Henzler, Niloy Mitra, Tobias Ritschel
We can successfully reconstruct 3D shapes from unstructured 2D images and extensively evaluate PlatonicGAN on a range of synthetic and real data sets achieving consistent improvements over baseline methods.
1 code implementation • 12 Nov 2018 • Pedro Hermosilla, Sebastian Maisch, Tobias Ritschel, Timo Ropinski
Thus, we suggest a two-stage operator comprising of a 3D network that first transforms the point cloud into a latent representation, which is later on projected to the 2D output image using a dedicated 3D-2D network in a second step.
1 code implementation • 5 Jun 2018 • Pedro Hermosilla, Tobias Ritschel, Pere-Pau Vázquez, Àlvar Vinacua, Timo Ropinski
We propose an efficient and effective method to learn convolutions for non-uniformly sampled point clouds, as they are obtained with modern acquisition techniques.
1 code implementation • 8 May 2018 • Carlo Innamorati, Tobias Ritschel, Tim Weyrich, Niloy J. Mitra
Convolutional neural networks (CNNs) handle the case where filters extend beyond the image boundary using several heuristics, such as zero, repeat or mean padding.
no code implementations • ICCV 2019 • Maxim Maximov, Laura Leal-Taixé, Mario Fritz, Tobias Ritschel
Second, we demonstrate how another network can be used to map from an image or video frames to a DAM network to reproduce this appearance, without using a lengthy optimization such as stochastic gradient descent (learning-to-learn).
1 code implementation • 23 Oct 2017 • Tuanfeng Y. Wang, Tobias Ritschel, Niloy J. Mitra
To the other hand, methods that are automatic and work on 'in the wild' Internet images, often extract only low-frequency lighting or diffuse materials.
Graphics
no code implementations • ICCV 2017 • Stamatios Georgoulis, Konstantinos Rematas, Tobias Ritschel, Mario Fritz, Tinne Tuytelaars, Luc van Gool
How much does a single image reveal about the environment it was taken in?
no code implementations • 27 Mar 2016 • Stamatios Georgoulis, Konstantinos Rematas, Tobias Ritschel, Mario Fritz, Luc van Gool, Tinne Tuytelaars
In this paper we are extracting surface reflectance and natural environmental illumination from a reflectance map, i. e. from a single 2D image of a sphere of one material under one illumination.
no code implementations • 19 Mar 2016 • Oliver Nalbach, Elena Arabadzhiyska, Dushyant Mehta, Hans-Peter Seidel, Tobias Ritschel
In computer vision, convolutional neural networks (CNNs) have recently achieved new levels of performance for several inverse problems where RGB pixel appearance is mapped to attributes such as positions, normals or reflectance.
no code implementations • 31 Jan 2016 • Konstantinos Rematas, Chuong Nguyen, Tobias Ritschel, Mario Fritz, Tinne Tuytelaars
We propose a technique to use the structural information extracted from a 3D model that matches the image object in terms of viewpoint and shape.
no code implementations • CVPR 2016 • Konstantinos Rematas, Tobias Ritschel, Mario Fritz, Efstratios Gavves, Tinne Tuytelaars
Undoing the image formation process and therefore decomposing appearance into its intrinsic properties is a challenging task due to the under-constraint nature of this inverse problem.
no code implementations • 12 Aug 2015 • Bojan Pepik, Rodrigo Benenson, Tobias Ritschel, Bernt Schiele
", and "what can the network learn?".
no code implementations • 17 Mar 2015 • Bojan Pepik, Michael Stark, Peter Gehler, Tobias Ritschel, Bernt Schiele
Object class detection has been a synonym for 2D bounding box localization for the longest time, fueled by the success of powerful statistical learning techniques, combined with robust image representations.
no code implementations • CVPR 2014 • Konstantinos Rematas, Tobias Ritschel, Mario Fritz, Tinne Tuytelaars
We propose a technique to use the structural information extracted from a set of 3D models of an object class to improve novel-view synthesis for images showing unknown instances of this class.