A Papier-Mache Approach to Learning 3D Surface Generation
| Autor: | Mathieu Aubry, Thibault Groueix, Vladimir G. Kim, Matthew Fisher, Bryan Russell |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
business.industry
Computer science Generalization ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Point cloud 020207 software engineering 02 engineering and technology Image segmentation Iterative reconstruction Morphing Parametric surface 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business Representation (mathematics) Parametrization Algorithm ComputingMethodologies_COMPUTERGRAPHICS |
| Zdroj: | 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. |
| DOI: | 10.1109/cvpr.2018.00030 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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