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

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This paper presents a comparison of machine learning (ML) methods used for three-dimensional localization of partial discharges (PD) in a power transformer tank. The study examines ML and deep learning (DL) methods, ranging from support vector machines (SV ...

The present work proposes a framework for nonlinear model order reduction based on a Graph Convolutional Autoencoder (GCA-ROM). In the reduced order modeling (ROM) context, one is interested in obtaining real -time and many-query evaluations of parametric ...

Photometric stereo, a computer vision technique for estimating the 3D shape of objects through images captured under varying illumination conditions, has been a topic of research for nearly four decades. In its general formulation, photometric stereo is an ...

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 ...

Example Data for DeepReefMap This dataset contains input videos in MP4 format taken with GoPro Hero 10 Cameras in Reefs in the Red Sea to demonstrate the DeepReefMap tool, which is described in the paper "Scalable Semantic 3D Mapping of Coral Reefs with De ...

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 ...

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 ...

Mesh manipulation is central to computational fluid dynamics. However, creating appropriate computational meshes often involves substantial manual intervention that has to be repeated each time the target shape changes. To address this problem, we propose ...

Despite their incredible performance, it is well reported that deep neural networks tend to be overoptimistic about their prediction confidence. Finding effective and efficient calibration methods for neural networks is therefore an important endeavour tow ...

We introduce Neural Network (NN for short) approximation architectures for the numerical solution of Boundary Integral Equations (BIEs for short). We exemplify the proposed NN approach for the boundary reduction of the potential problem in two spatial dime ...

To obtain a more complete understanding of material microstructure at the nanoscale and to gain profound insights into their properties, there is a growing need for more efficient and precise methods that can streamline the process of 3D imaging using a tr ...

We propose a novel system leveraging deep learning-based methods to predict urban traffic accidents and estimate their severity. The major challenge is the data imbalance problem in traffic accident prediction. The problem is caused by numerous zero values ...

The choice of the shape parameter highly effects the behaviour of radial basis function (RBF) approximations, as it needs to be selected to balance between the ill-conditioning of the interpolation matrix and high accuracy. In this paper, we demonstrate ho ...

We consider the problem of compressing an information source when a correlated one is available as side information only at the decoder side, which is a special case of the distributed source coding problem in information theory. In particular, we consider ...

Reading out neuronal activity from three-dimensional (3D) functional imaging requires segmenting and tracking individual neurons. This is challenging in behaving animals if the brain moves and deforms. The traditional approach is to train a convolutional n ...

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 ...

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 ...

With the advent of data science, the analysis of network or graph data has become a very timely research problem. A variety of recent works have been proposed to generalize neural networks to graphs, either from a spectral graph theory or a spatial perspec ...

In the recent years, Deep Neural Networks (DNNs) have managed to succeed at tasks that previously appeared impossible, such as human-level object recognition, text synthesis, translation, playing games and many more. In spite of these major achievements, o ...

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 ...

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

Chattez avec Graph Search

Partial discharge localization in power transformer tanks using machine learning methods

A graph convolutional autoencoder approach to model order reduction for parametrized PDEs

Aggregating Spatial and Photometric Context for Photometric Stereo

Semantic Shape Editing with Parametric Implicit Templates

Data for Paper "Scalable Semantic 3D Mapping of Coral Reefs with Deep Learning"

Deep Learning for 3D Surface Modelling and Reconstruction

Region Extraction in Mesh Intersection

Latent Representation of Computational Fluid Dynamics Meshes and Application to Airfoil Aerodynamics

Expectation consistency for calibration of neural networks

ReLU Neural Network Galerkin BEM

3D reconstruction of curvilinear one-dimensional objects viewed in transmission electron microscopy

From Prediction to Prevention: Leveraging Deep Learning in Traffic Accident Prediction Systems

A new variable shape parameter strategy for RBF approximation using neural networks

Neural Distributed Image Compression with Cross-Attention Feature Alignment

Automated neuron tracking inside moving and deforming C. elegans using deep learning and targeted augmentation

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

Differentiable Signed Distance Function Rendering

iPool--Information-Based Pooling in Hierarchical Graph Neural Networks

Theory of Deep Learning: Neural Tangent Kernel and Beyond

An a posteriori error estimator for isogeometric analysis on trimmed geometries