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Publications associées (31)

Remarks on Poincare and interpolation estimates for Truncated Hierarchical B-splines

Annalisa Buffa

This paper should be considered as an addendum to [A. Buffa and C. Giannelli, Adaptive isogeometric methods with hierarchical splines: Error estimator and convergence, Math. Models Methods Appl. Sci. 26 (2016) 1-25] and [A. Buffa and C. Giannelli, Adaptive ...

2021

Light Path Gradients for Forward and Inverse Rendering

Tizian Lucien Zeltner

Physically based rendering is a process for photorealistic digital image synthesis and one of the core problems in computer graphics. It involves simulating the light transport, i.e. the emission, propagation, and scattering of light through a virtual scen ...

EPFL2021

Fast end-to-end learning on protein surfaces

Bruno Emanuel Ferreira De Sousa Correia, Freyr Sverrisson

Proteins' biological functions are defined by the geometric and chemical structure of their 3D molecular surfaces. Recent works have shown that geometric deep learning can be used on mesh-based representations of proteins to identify potential functional s ...

IEEE COMPUTER SOC2021

Neural Parts: Learning Expressive 3D Shape Abstractions with Invertible Neural Networks

Angelos Katharopoulos, Andreas Geiger, Despoina Paschalidou

Impressive progress in 3D shape extraction led to representations that can capture object geometries with high fidelity. In parallel, primitive-based methods seek to represent objects as semantically consistent part arrangements. However, due to the simpli ...

IEEE COMPUTER SOC2021

Learning Lipschitz-Controlled Activation Functions in Neural Networks for Plug-and-Play Image Reconstruction Methods

Michaël Unser, Dimitris Perdios, Pakshal Narendra Bohra, Alexis Marie Frederic Goujon, Sébastien Alexandre Emery

Ill-posed linear inverse problems are frequently encountered in image reconstruction tasks. Image reconstruction methods that combine the Plug-and-Play (PnP) priors framework with convolutional neural network (CNN) based denoisers have shown impressive per ...

2021

How to teach neural networks to mesh: Application on 2-D simplicial contours

François Avellan, Pascal Clausen, Alexis Papagiannopoulos

A machine learning meshing scheme for the generation of 2-D simplicial meshes is proposed based on the predictions of neural networks. The data extracted from meshed contours are utilized to train neural networks which are used to approximate the number of ...

2021

2d-3d sculpture paintings

Sabine Süsstrunk, Roger Hersch, Mahmut Sami Arpa

We disclose a method for synthesizing by computer an artwork where 2D and 3D contents are integrated within a same composition. Such an artwork creates plausible effects for the viewers by showing a different relationship between 2D and 3D at each viewing ...

2020

High-order essentially nonoscillatory methods based on radial basis functions

Fabian Mönkeberg

Essentially nonoscillatory (ENO) and weighted ENO (WENO) methods on equidistant Cartesian grids are widely employed to solve partial differential equations with discontinuous solutions. However, stable ENO/WENO methods on unstructured grids are less well s ...

EPFL2020

Two-Dimensional RBF-ENO Method on Unstructured Grids

Jan Sickmann Hesthaven, Fabian Mönkeberg

Essentially non-oscillatory (ENO) and weighted ENO (WENO) methods on equidistant Cartesian grids are widely used to solve partial differential equations with discontinuous solutions. However, stable ENO/WENO methods on unstructured grids are less well stud ...

SPRINGER/PLENUM PUBLISHERS2020

Assessing the Efficacy of a GPU-Based MW-FDTD Method for Calculating Lightning Electromagnetic Fields Over Large-Scale Terrains

Farhad Rachidi-Haeri, Marcos Rubinstein, Mohammad Azadifar, Hamidreza Karami

This article presents for the first time an OpenACC (Open accelerators)-aided Graphics Processing Unit (GPU)-based approach adopting a 3D moving window finite difference time domain (MW-FDTD) method for calculating lightning electromagnetic fields over lar ...

2020

On the Efficiency of OpenACC-aided GPU-Based FDTD Approach: Application to Lightning Electromagnetic Fields

Farhad Rachidi-Haeri, Mohammad Azadifar, Hamidreza Karami

An open accelerator (OpenACC)-aided graphics processing unit (GPU)-based finite difference time domain (FDTD) method is presented for the first time for the 3D evaluation of lightning radiated electromagnetic fields along a complex terrain with arbitrary t ...

MDPI2020

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