Mediaspace scheduled maintenance: Aug 25, 2026 07:00 - 12:00 AM. During this time, videos will be temporarily unavailable. Check status updates.

Concept

Polygonal modeling

Related publications (30)

Semantic Shape Editing with Parametric Implicit Templates

Uday Kusupati

We propose a semantic shape editing method to edit 3D triangle meshes using parametric implicit surface templates, benefiting from the many advantages offered by analytical implicit representations, such as infinite resolution and boolean or blending opera ...

ACM Association for Computing Machinery2024

Automatic Parameterization for Aerodynamic Shape Optimization via Deep Geometric Learning

Pascal Fua, Zhen Wei

We propose two deep learning models that fully automate shape parameterization for aerodynamic shape optimization. Both models are optimized to parameterize via deep geometric learning to embed human prior knowledge into learned geometric patterns, elimina ...

2023

Region Extraction in Mesh Intersection

Annalisa Buffa, Pablo Antolin Sanchez, Emiliano Cirillo

Region extraction is a very common task in both Computer Science and Engineering with several applications in object recognition and motion analysis, among others. Most of the literature focuses on regions delimited by straight lines, often in the special ...

2023

Automated post-earthquake damage assessment of stone masonry buildings integrating machine learning, computer vision, and physics-based modeling

Bryan German Pantoja Rosero

Current post-earthquake damage assessment methodologies are not only time-consuming but also subjective in nature and difficult to document. Recent advancements in artificial intelligence and technological devices make it possible to accomplish this task a ...

EPFL2023

Machine-Learning-Enhanced Procedural Modeling for 4D Historical Cities Reconstruction

Frédéric Kaplan, Isabella Di Lenardo, Rémi Guillaume Petitpierre, Beatrice Vaienti

The generation of 3D models depicting cities in the past holds great potential for documentation and educational purposes. However, it is often hindered by incomplete historical data and the specialized expertise required. To address these challenges, we p ...

2023

A virtual microstructure generator for 3D stone masonry walls

Katrin Beyer, Mahmoud S. M. Shaqfa

Detailed micromodel simulations of stone masonry walls require as input a 3D mesh that represents a realistic arrangement of stones in the masonry wall. In this paper, we constructed the first 3D masonry microstructures to derive 2D and 3D finite or discre ...

ELSEVIER2022

Meshing of Spiny Neuronal Morphologies using Union Operators

Felix Schürmann, Marwan Muhammad Ahmed Abdellah, Nadir Roman Guerrero, Alessandro Enrico Foni, Elvis Boci, Juan José García Cantero

Neurons are characterized by thin and long interleaving arborizations in which creating accurate mesh models of their cellular membranes is challenging. While union operators are central for CAD/CAM modeling and computer graphics applications, their applic ...

2022

Synthesis and Analysis of 3D shapes with Geometric Deep Learning in Computer-Aided Engineering

Edoardo Remelli

In this thesis, we advocate that Computer-Aided Engineering could benefit from a Geometric Deep Learning revolution, similarly to the way that Deep Learning revolutionized Computer Vision. To do so, we consider a variety of Computer-Aided Engineering pr ...

EPFL2022

Robust Isometric Non-Rigid Structure-From-Motion

Shaifali Parashar

Non-Rigid Structure-from-Motion (NRSfM) reconstructs a deformable 3D object from keypoint correspondences established between monocular 2D images. Current NRSfM methods lack statistical robustness, which is the ability to cope with correspondence errors. T ...

IEEE COMPUTER SOC2022

Differentiable Signed Distance Function Rendering

Wenzel Alban Jakob, Delio Aleardo Vicini, Sébastien Nicolas Speierer

Physically-based differentiable rendering has recently emerged as an attractive new technique for solving inverse problems that recover complete 3D scene representations from images. The inversion of shape parameters is of particular interest but also pose ...

ASSOC COMPUTING MACHINERY2022

Fast 3D Modeling of Prosthetic Robotic Hands Based on a Multi-Layer Deformable Design

Daniel Thalmann

Current research of designing prosthetic robotic hands mainly focuses on improving their functionality by devising new mechanical structures and actuation systems. Most of existing work relies on a single structure/system (e.g., bone-only or tissue-only) a ...

WHIOCE PUBLISHING PTE LTD, SINGAPORE2022

An a posteriori error estimator for isogeometric analysis on trimmed geometries

Annalisa Buffa, Rafael Vazquez Hernandez, Ondine Gabrielle Chanon

Trimming consists of cutting away parts of a geometric domain, without reconstructing a global parametrization (meshing). It is a widely used operation in computer-aided design, which generates meshes that are unfitted with the described physical object. T ...

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

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

Sketch2Mesh: Reconstructing and Editing 3D Shapes from Sketches

Pascal Fua, Benoît Alain René Guillard, Edoardo Remelli, Pierre Mathieu Yvernay

Reconstructing 3D shape from 2D sketches has long been an open problem because the sketches only provide very sparse and ambiguous information. In this paper, we use an encoder/decoder architecture for the sketch to mesh translation. When integrated into a ...

IEEE2021

A Non-Exponential Transmittance Model for Volumetric Scene Representations

Wenzel Alban Jakob, Delio Aleardo Vicini

We introduce a novel transmittance model to improve the volumetric representation of 3D scenes. The model can represent opaque surfaces in the volumetric light transport framework. Volumetric representations are useful for complex scenes, and become increa ...

ASSOC COMPUTING MACHINERY2021

Tessellation and lmprovement of Simplicial Meshes using Neural Networks

Alexis Papagiannopoulos

Modern mesh generation addresses the development of robust algorithms that construct a discrete representation of the geometry into polytopal elements conforming to divergent properties: (i) fidelity to complex geometrical features, (ii) support for high s ...

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

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

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