Details

Autor(en) / Beteiligte

• Groueix, Thibault
• Fisher, Matthew
• Kim, Vladimir G.
• Russell, Bryan C.
• Aubry, Mathieu

Titel

A Papier-Mache Approach to Learning 3D Surface Generation

Ist Teil von

• 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018, p.216-224

Ort / Verlag

IEEE

Erscheinungsjahr

2018

Quelle

IEL

Beschreibungen/Notizen

• 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) autoencoding 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.