AtlasNet: A Papier-Mâché Approach to Learning 3D Surface Generation

We introduce a method for learning to generate the surface of 3D shapes. Our approach represents a 3D shape as a collection of parametric surface elements and, in contrast to methods generating voxel grids or point clouds, naturally infers a surface representation of the shape. Beyond its novelty, o...

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Bibliographic Details
Published in:arXiv.org 2018-07
Main Authors: Thibault Groueix, Fisher, Matthew, Kim, Vladimir G, Russell, Bryan C, Aubry, Mathieu
Format: Article
Language:English
Subjects:
Image reconstruction
Image segmentation
Morphing
Parameterization
Shape memory
Online Access:Get full text
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Summary:We introduce a method for learning to generate the surface of 3D shapes. Our approach represents a 3D shape as a collection of parametric surface elements and, in contrast to methods generating voxel grids or point clouds, naturally infers a surface representation of the shape. Beyond its novelty, our new shape generation framework, AtlasNet, comes with significant advantages, such as improved precision and generalization capabilities, and the possibility to generate a shape of arbitrary resolution without memory issues. We demonstrate these benefits and compare to strong baselines on the ShapeNet benchmark for two applications: (i) auto-encoding shapes, and (ii) single-view reconstruction from a still image. We also provide results showing its potential for other applications, such as morphing, parametrization, super-resolution, matching, and co-segmentation.
ISSN:2331-8422