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

Abstract : 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.
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Communication dans un congrès
CVPR 2018, Jun 2018, Salt Lake City, United States
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Thibault Groueix, Matthew Fisher, Vladimir Kim, Bryan Russell, Mathieu Aubry. AtlasNet: A Papier-Mâché Approach to Learning 3D Surface Generation. CVPR 2018, Jun 2018, Salt Lake City, United States. 〈hal-01718933〉

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