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Auteur Marie-Julie Rakotosaona |
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Learning-based representations and methods for 3D shape analysis, manipulation and reconstruction / Marie-Julie Rakotosaona (2021)
Titre : Learning-based representations and methods for 3D shape analysis, manipulation and reconstruction Type de document : Thèse/HDR Auteurs : Marie-Julie Rakotosaona, Auteur ; Maks Ovsjanikov, Directeur de thèse Editeur : Palaiseau : Ecole Polytechnique EP Année de publication : 2021 Importance : 148 p. Format : 21 x 30 cm Note générale : bibliographie
These de doctorat de l’Institut Polytechnique de Paris préparée à l’Ecole polytechnique spécialité InformatiqueLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] apprentissage profond
[Termes IGN] figure géométrique
[Termes IGN] filtrage de points
[Termes IGN] filtrage du bruit
[Termes IGN] image 3D
[Termes IGN] interpolation
[Termes IGN] maillage
[Termes IGN] maille triangulaire
[Termes IGN] reconstruction 3D
[Termes IGN] semis de points
[Termes IGN] triangulation de Delaunay
[Termes IGN] voxelIndex. décimale : THESE Thèses et HDR Résumé : (auteur) Efficiently processing and analysing 3D data is a crucial challenge in modern applications as 3D shapes are becoming more and more widespread with the proliferation of acquisition devices and modeling tools. While successes of 2D deep learning have become commonplace and surround our daily life, applications that involve 3D data are lagging behind. Due to the more complex non-uniform structure of 3D shapes, successful methods from 2D deep learning cannot be easily extended and there is a strong demand for novel approaches that can both exploit and enable learning using geometric structure. Moreover, being able to handle the various existing representations of 3D shapes such as point clouds and meshes, as well as the artefacts produced from 3D acquisition devices increases the difficulty of the task. In this thesis, we propose systematic approaches that fully exploit geometric information of 3D data in deep learning architectures. We contribute to point cloud denoising, shape interpolation and shape reconstruction methods. We observe that deep learning architectures facilitate learning the underlying surface structure on point clouds that can then be used for denoising as well as shape interpolation. Encoding local patch-based learned priors, as well as complementary geometric information such as edge lengths, leads to powerful pipelines that generate realistic shapes. The key common thread throughout our contributions is facilitating seamless conversion between different representations of shapes. In particular, while using deep learning on triangle meshes is highly challenging due to their combinatorial nature we introduce methods inspired from geometry processing that enable the creation and manipulation of triangle faces. Our methods are robust and generalize well to unseen data despite limited training sets. Our work, therefore, paves the way towards more general, robust and universally useful manipulation of 3D data. Note de contenu : 1- Introduction
2- Introduction en français
3- PointCleanNet: Learning to denoise and remove outliers from dense point clouds
4- Intrinsic point cloud interpolation via dual latent space navigation
5- Learning Delaunay surface elements for mesh reconstruction
6- Differentiable surface triangulation
7- ConclusionNuméro de notice : 28649 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse française Note de thèse : Thèse de Doctorat : Informatique : Ecole Polytechnique : 2021 Organisme de stage : Laboratoire d'informatique de l'École polytechnique DOI : sans En ligne : https://tel.hal.science/tel-03541331/ Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99744