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A neuron membrane mesh representation for visualization of electrophysiological simulations
Publications associées (55)
Semantic Shape Editing with Parametric Implicit Templates
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 ...
2024Deep Learning for 3D Surface Modelling and Reconstruction
In recent years, there has been a significant revolution in the field of deep learning, which has demonstrated its effectiveness in automatically capturing intricate patterns from large datasets. However, the majority of these successes in Computer Vision ...
EPFL2023A 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 ...
2023Automated post-earthquake damage assessment of stone masonry buildings integrating machine learning, computer vision, and physics-based modeling
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 ...
EPFL2023Meshing of Spiny Neuronal Morphologies using Union Operators
Felix Schürmann, Marwan Muhammad Ahmed Abdellah, Elvis Boci, Juan José García Cantero, Alessandro Enrico Foni, Nadir Roman Guerrero
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 ...
The Eurographics Association2022Synthesis and Analysis of 3D shapes with Geometric Deep Learning in Computer-Aided Engineering
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 pro ...
EPFL2022A 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 ...
ELSEVIER2022Differentiable 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 MACHINERY2022HybridSDF: Combining Deep Implicit Shapes and Geometric Primitives for 3D Shape Representation and Manipulation
Pascal Fua, Nicolas Talabot, Subeesh Vasu, Artem Lukoianov
Deep implicit surfaces excel at modeling generic shapes but do not always capture the regularities present in manufactured objects, which is something simple geometric primitives are particularly good at. In this paper, we propose a representation combinin ...
2022Sketch2Mesh: Reconstructing and Editing 3D Shapes from Sketches
Pascal Fua, Benoît Alain René Guillard, Pierre Mathieu Yvernay, Edoardo Remelli
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