Search Results for author: Tobias Ritschel

Found 28 papers, 8 papers with code

Data-driven deep density estimation

1 code implementation23 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.

Density Estimation

Unsupervised Learning of 3D Object Categories from Videos in the Wild

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.

Generative Modelling of BRDF Textures from Flash Images

no code implementations23 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.

Neural BRDF Representation and Importance Sampling

no code implementations11 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.

Fermi surface chirality induced in a TaSe$_{2}$ monosheet formed by a Ta/ Bi$_{2}$Se$_{3}$ interface reaction

no code implementations14 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

Curiosity-driven 3D Scene Structure from Single-image Self-supervision

no code implementations2 Dec 2020 David Griffiths, Jan Boehm, Tobias Ritschel

The resulting system converts 2D images of different virtual or real images into complete 3D scenes, learned only from 2D images of those scenes.

Deep Generative Modelling of Human Reach-and-Place Action

no code implementations5 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.

X-Fields: Implicit Neural View-, Light- and Time-Image Interpolation

no code implementations1 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.

Image Interpolation

Finding Your (3D) Center: 3D Object Detection Using a Learned Loss

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.

3D Object Detection

Learning a Neural 3D Texture Space from 2D Exemplars

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.

Neural View-Interpolation for Sparse Light Field Video

no code implementations30 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.

Total Denoising: Unsupervised Learning of 3D Point Cloud Cleaning

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.


Escaping Plato's Cave: 3D Shape From Adversarial Rendering

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.

Deep-learning the Latent Space of Light Transport

1 code implementation12 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.

Monte Carlo Convolution for Learning on Non-Uniformly Sampled Point Clouds

1 code implementation5 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.

Point Cloud Segmentation

Learning on the Edge: Explicit Boundary Handling in CNNs

no code implementations8 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.

Colorization Disparity Estimation +1

Deep Appearance Maps

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).

Joint Material and Illumination Estimation from Photo Sets in the Wild

1 code implementation23 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.


DeLight-Net: Decomposing Reflectance Maps into Specular Materials and Natural Illumination

no code implementations27 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.

Deep Shading: Convolutional Neural Networks for Screen-Space Shading

no code implementations19 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.

Image Generation

Novel Views of Objects from a Single Image

no code implementations31 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.

Novel View Synthesis

Deep Reflectance Maps

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.

3D Object Class Detection in the Wild

no code implementations17 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.

Object Detection Viewpoint Estimation

Image-based Synthesis and Re-Synthesis of Viewpoints Guided by 3D Models

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.

Novel View Synthesis Super-Resolution

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