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.

该研究介绍了一种生成 3D 形状表面的方法——AtlasNet，它将 3D 形状表示为参数化表面元素的集合，相较于生成体素网格或点云的方法，具有更好的精度和泛化能力，并可以生成任意分辨率的形状。该方法在自编码形状和单视图重建方面取得了比较优异的结果，并且在变形、参数化、超分辨率、匹配和共同分割等方面显示其潜力。

AtlasNet: 学习三维表面生成的纸浆方法