Implicit meshes : unifying implicit and explicit surface representations for 3D reconstruction and tracking

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Unstable periodic orbits (UPOs) are believed to be the underlying dynamical structures of spatiotemporal chaos and turbulence. Finding these UPOs is, however, notoriously difficult. Matrix-free loop convergence algorithms deform entire space-time fields (l ...

Path optimization is a fundamental concern across various real-world scenarios, ranging from traffic congestion issues to efficient data routing over the Internet. The traffic assignment problem (TAP) is a classic continuous optimization problem in this fi ...

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 ...

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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 ...

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The goal of this thesis is the development and the analysis of numerical methods for problems where the unknown is a curve on a smooth manifold. In particular, the thesis is structured around the three following problems: homotopy continuation, curve inter ...

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 ...

We introduce robust principal component analysis from a data matrix in which the entries of its columns have been corrupted by permutations, termed Unlabeled Principal Component Analysis (UPCA). Using algebraic geometry, we establish that UPCA is a well-de ...

Computing light reflection from rough surfaces is an important topic in computer graphics. Reflection models developed based on geometric optics fail to capture wave effects such as diffraction and interference, while existing models based on physical opti ...

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 ...

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The performance of modern object detectors drops when the test distribution differs from the training one. Most of the methods that address this focus on object appearance changes caused by, e.g., different illumination conditions, or gaps between syntheti ...

Human hands are highly articulated and versatile at handling objects. Jointly estimating the 3D poses of a hand and the object it manipulates from a monocular camera is challenging due to frequent occlusions. Thus, existing methods often rely on intermedia ...

Non-convex constrained optimization problems have become a powerful framework for modeling a wide range of machine learning problems, with applications in k-means clustering, large- scale semidefinite programs (SDPs), and various other tasks. As the perfor ...

Turning pass-through network architectures into iterative ones, which use their own output as input, is a well-known approach for boosting performance. In this paper, we argue that such architectures offer an additional benefit: The convergence rate of the ...

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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 ...

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 ...

Implicit meshes : unifying implicit and explicit surface representations for 3D reconstruction and tracking

Chat with Graph Search

Data-driven Guessing and Gluing of Unstable Periodic Orbits

Integer traffic assignment problem: Algorithms and insights on random graphs

Semantic Shape Editing with Parametric Implicit Templates

Scalable constrained optimization

Aggregating Spatial and Photometric Context for Photometric Stereo

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

Adaptive Finite Elements with Large Aspect Ratio. Application to Aluminium Electrolysis

Retraction-based numerical methods for continuation, interpolation and time integration on manifolds

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

Unlabeled Principal Component Analysis and Matrix Completion

A Full-Wave Reference Simulator for Computing Surface Reflectance

Automatic Parameterization for Aerodynamic Shape Optimization via Deep Geometric Learning

Incorporating Projective Geometry into Deep Learning

Learning Transformations To Reduce the Geometric Shift in Object Detection

HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields

Augmented Lagrangian Methods for Provable and Scalable Machine Learning

Enabling Uncertainty Estimation in Iterative Neural Networks

CLIP the Gap: A Single Domain Generalization Approach for Object Detection

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

Meshing of Spiny Neuronal Morphologies using Union Operators