Point2mesh: A self-prior for deformable meshes
In this paper, we introduce Point2Mesh, a technique for reconstructing a surface mesh from
an input point cloud. Instead of explicitly specifying a prior that encodes the expected shape …
an input point cloud. Instead of explicitly specifying a prior that encodes the expected shape …
Edge-aware point set resampling
Points acquired by laser scanners are not intrinsically equipped with normals, which are
essential to surface reconstruction and point set rendering using surfels. Normal estimation …
essential to surface reconstruction and point set rendering using surfels. Normal estimation …
An overview on uncertainty quantification and probabilistic learning on manifolds in multiscale mechanics of materials
C Soize - Mathematics and Mechanics of Complex Systems, 2023 - msp.org
An overview of the author's works, many of which were carried out in collaboration, is
presented. The first part concerns the quantification of uncertainties for complex engineering …
presented. The first part concerns the quantification of uncertainties for complex engineering …
Pointpronets: Consolidation of point clouds with convolutional neural networks
R Roveri, AC Öztireli, I Pandele… - Computer Graphics …, 2018 - Wiley Online Library
With the widespread use of 3D acquisition devices, there is an increasing need of
consolidating captured noisy and sparse point cloud data for accurate representation of the …
consolidating captured noisy and sparse point cloud data for accurate representation of the …
Probabilistic-learning-based stochastic surrogate model from small incomplete datasets for nonlinear dynamical systems
We consider a high-dimensional nonlinear computational model of a dynamical system,
parameterized by a vector-valued control parameter, in the presence of uncertainties …
parameterized by a vector-valued control parameter, in the presence of uncertainties …
Nonlinear stochastic dynamics of detuned bladed-disks with uncertain mistuning and detuning optimization using a probabilistic machine learning tool
E Capiez-Lernout, C Soize - International Journal of Non-Linear Mechanics, 2022 - Elsevier
The paper deals with the nonlinear stochastic dynamics concerning the detuning
optimization in presence of random mistuning of bladed-disks with geometrical …
optimization in presence of random mistuning of bladed-disks with geometrical …
A survey of blue-noise sampling and its applications
In this paper, we survey recent approaches to blue-noise sampling and discuss their
beneficial applications. We discuss the sampling algorithms that use points as sampling …
beneficial applications. We discuss the sampling algorithms that use points as sampling …
GPF: GMM-inspired feature-preserving point set filtering
Point set filtering, which aims at reconstructing noise-free point sets from their corresponding
noisy inputs, is a fundamental problem in 3D geometry processing. The main challenge of …
noisy inputs, is a fundamental problem in 3D geometry processing. The main challenge of …
Blue-noise point sampling using kernel density model
R Fattal - ACM Transactions on Graphics (TOG), 2011 - dl.acm.org
Stochastic point distributions with blue-noise spectrum are used extensively in computer
graphics for various applications such as avoiding aliasing artifacts in ray tracing, halftoning …
graphics for various applications such as avoiding aliasing artifacts in ray tracing, halftoning …
Variational blue noise sampling
Blue noise point sampling is one of the core algorithms in computer graphics. In this paper,
we present a new and versatile variational framework for generating point distributions with …
we present a new and versatile variational framework for generating point distributions with …