AtlasNet swMATH ID: 36368 Software Authors: Groueix, Thibault; Fisher, Matthew; Kim, Vladimir G.; Russell, Bryan C.; Aubry, Mathieu Description: 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, 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. Homepage: https://arxiv.org/abs/1802.05384 Source Code: https://github.com/ThibaultGROUEIX/AtlasNet Keywords: Computer Vision; Pattern Recognition; arXiv_cs.CV Related Software: Adam; DeepSDF; FoldingNet; PointNet; PyTorch; ShapeNet; SimNet; darch; F3DAM; DiffSharp; TOuNN; CycleGAN; Tensor2Tensor; PINN; torchdiffeq; PILCO; U-Net; pix2pix; PDE-Net; hp-VPINNs Cited in: 3 Documents all top 5 Cited by 8 Authors 1 Ferrari, Vittorio 1 Gadelha, Matheus 1 Henderson, Paul 1 Herrmann, Leon 1 Kollmannsberger, Stefan 1 Maji, Subhransu 1 Rai, Aartika 1 Wang, Rui Cited in 2 Serials 2 International Journal of Computer Vision 1 Computational Mechanics Cited in 2 Fields 2 Computer science (68-XX) 1 Mechanics of deformable solids (74-XX) Citations by Year