Publications related to Parametric surface

The effect of smooth parametrizations on nonconvex optimization landscapes

We develop new tools to study landscapes in nonconvex optimization. Given one optimization problem, we pair it with another by smoothly parametrizing the domain. This is either for practical purposes (e.g., to use smooth optimization algorithms with good g ...

Springer Heidelberg2024

A method and system for optimizing design, implantation, and stimulation protocols of electrodes stimulating the nervous system

Silvestro Micera, Simone Romeni

A method for optimizing at least one of a geometry, an implantation procedure, and/or stimulation protocol of one or more electrodes for an electrical stimulation of a target structure in a nervous system of a living being by a computer device, the method ...

2024

Search for narrow resonances in the b-tagged dijet mass spectrum in proton-proton collisions at √s=13 TeV

Matthias Finger, Konstantin Androsov, Qian Wang, Jan Steggemann, Anna Mascellani, Yiming Li, Varun Sharma, Xin Chen, Arvind Shah, Rakesh Chawla, Jian Wang, João Miguel das Neves Duarte, Tagir Aushev, Matthias Wolf, Yi Zhang, Lei Zhang, Tian Cheng, Yixing Chen, Werner Lustermann, Andromachi Tsirou, Alexis Kalogeropoulos, Andrea Rizzi, Ioannis Papadopoulos, Paolo Ronchese, Hua Zhang, Leonardo Cristella, Siyuan Wang, Jessica Prisciandaro, Tao Huang, David Vannerom, Michele Bianco, Sebastiana Gianì, Sun Hee Kim, Davide Di Croce, Kun Shi, Wei Shi, Guido Andreassi, Abhisek Datta, Jian Zhao, Federica Legger, Gabriele Grosso, Ji Hyun Kim, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,

A method and system for enforcing smoothness constraints on surface meshes from a graph convolutional neural network

Pascal Fua, Pamuditha Udaranga Wickramasinghe

A method for enforcing smoothness constraints on surface meshes produced by a Graph Convolutional Neural Network (GCNN) including the steps of reading image data from a memory, the image data including two-dimensional image data representing a three-dimens ...

2023

Adapting Haptic Feedback for Guided Meditation

Cédric Duchene, Nicolas Henchoz, Emily Clare Groves, Romain Simon Collaud, Andreas Sonderegger, Yoann Pierre Douillet

Technology supporting meditation is a multimillion-dollar market that continues to grow. There is also strong academic interest to understand and improve the impact technology can have for the user experience of practitioners. However, little work investig ...

2023

Robust numerical integration on curved polyhedra based on folded decompositions

Annalisa Buffa, Pablo Antolin Sanchez, Xiaodong Wei

We present a novel method to perform numerical integration over curved polyhedra enclosed by high-order parametric surfaces. Such a polyhedron is first decomposed into a set of triangular and/or rectangular pyramids, whose certain faces correspond to the g ...

ELSEVIER SCIENCE SA2022

Alpha-NML Universal Predictors

Michael Christoph Gastpar, Marco Bondaschi

Inspired by Sibson’s alpha-mutual information, we introduce a new parametric class of universal predictors. This class interpolates two well-known predictors, the mixture estimator, that includes the Laplace and the Krichevsky-Trofimov predictors, and the ...

2022

Learning to Represent and Reconstruct 3D Deformable Objects

Jan Bednarík

Representing and reconstructing 3D deformable shapes are two tightly linked problems that have long been studied within the computer vision field. Deformable shapes are truly ubiquitous in the real world, whether be it specific object classes such as human ...

EPFL2022

Design and evaluation of digital tools to expand experience in vocational education

Kevin Gonyop Kim

Vocational education and training (VET) is the most popular form of upper-secondary education in Switzerland, with more than two-thirds of the teenagers enrolling in a VET program after finishing their compulsory education. The vast majority of the Swiss V ...

EPFL2022

Strategic Insights into Engineering Parameters Affecting Cell Type-Specific Uptake of DNA-Based Nanomaterials

Maartje Martina Cornelia Bastings, Alice Comberlato, Hugo José Rodriguez Franco, Marianna Mainardi Koga

DNA-based nanomaterials are gaining popularity as uniform and programmable bioengineering tools as a result of recent solutions to their weak stability under biological conditions. The DNA nanotechnology platform uniquely allows decoupling of engineering p ...

American Chemical Society2022

A method to numerically determine the secondary electron yield considering effects of the surface morphology

Guang-Yu Sun, Ning Yang

Secondary electron emission (SEE) of solid materials due to electron bombardment is influenced by numerous properties of materials, where the surface condition plays a critical role in the value of secondary electron yield (SEY). Here, a 3D random microstr ...

2021

A new characterization approach to study the mechanical behavior of silicon nanowires

Yusuf Leblebici, Zuhal Tasdemir, Mohammad Nasr Esfahani, Mustafa Yilmaz

This work proposes a new approach to characterize the mechanical properties of nanowires based on a combination of nanomechanical measurements and models. Silicon nanowires with a critical dimension of 90 nm and a length of 8 mu m obtained through a monoli ...

SPRINGER HEIDELBERG2021

Generation of elastic geodesic gridshells with anisotropic cross sections

Corentin Jean Dominique Fivet, Nicolas Robin Montagne, Olivier Baverel

Geodesic gridshells are shell structures made of continuous elements following geodesic lines. Their properties ease the use of beams with anisotropic cross-sections by avoiding bending about their strong axis. However, such bending may arise when flatteni ...

2021

Leveraging Spatial and Photometric Context for Calibrated Non-Lambertian Photometric Stereo

Pascal Fua, Engin Türetken, David Honzátko

The problem of estimating a surface shape from its observed reflectance properties still remains a challenging task in computer vision. The presence of global illumination effects such as inter-reflections or cast shadows makes the task particularly diffic ...

IEEE COMPUTER SOC2021

In vivo macromolecule signals in rat brain 1 H‐MR spectra at 9.4T: Parametrization, spline baseline estimation, and T 2 relaxation times

Lijing Xin, Cristina Ramona Cudalbu, Bernard Lanz, Veronika Rackayová, Dunja Simicic

Purpose Reliable detection and fitting of macromolecules (MM) are crucial for accurate quantification of brain short-echo time (TE) 1H-MR spectra. An experimentally acquired single MM spectrum is commonly used. Higher spectral resolution at ultra-high fiel ...

Wiley2021

Landscape and training regimes in deep learning

Matthieu Wyart, Mario Geiger, Leonardo Petrini

Deep learning algorithms are responsible for a technological revolution in a variety oftasks including image recognition or Go playing. Yet, why they work is not understood.Ultimately, they manage to classify data lying in high dimension – a feat generical ...

2021

UCLID-Net: Single View Reconstruction in Object Space

Pascal Fua, Benoît Alain René Guillard, Edoardo Remelli

Most state-of-the-art deep geometric learning single-view reconstruction approaches rely on encoder-decoder architectures that output either shape parametrizations or implicit representations. However, these representations rarely preserve the Euclidean st ...

2020

Tolerancing the surface form of aspheric microlenses manufactured by wafer-level optics techniques

Toralf Scharf, Wilfried Noell, Jeremy Béguelin

Tolerancing is an important step toward the fabrication of high-quality and cost-effective lens surfaces. It is critical for wafer-level optics, when up to tens of thousands microlenses are fabricated in parallel and whose surfaces cannot be formed individ ...

OPTICAL SOC AMER2020

Non-contact polishing of a crystalline layer or substrate by ion beam etching

Niels Quack, Teodoro Graziosi, Sichen Mi

Polishing method comprising the steps of: - providing at least one crystalline layer or substrate, the at least one crystalline layer or substrate extending in at least one plane, and including at least one outer surface and at least one depression extendi ...

2020

Shape Reconstruction by Learning Differentiable Surface Representations

Pascal Fua, Mathieu Salzmann, Shaifali Parashar, Jan Bednarík, Erhan Gündogdu

Generative models that produce point clouds have emerged as a powerful tool to represent 3D surfaces, and the best current ones rely on learning an ensemble of parametric representations. Unfortunately, they offer no control over the deformations of the su ...

IEEE2020