mlsanityData to 7 Oct 2026

AtlasNet: A Papier-Mach'e Approach to Learning 3D Surface Generation

arXiv 1802.05384 ↗First posted 15 Feb 2018cs.CVcited by 539

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Cited by 36 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

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.

Key facts. Cited by 539 papers in the map (data through 7 Oct 2026). First posted on arXiv 15 Feb 2018.

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Citing papers per year: 2021: 139; 2022: 137; 2023: 119; 2024: 70; 2025: 47; 2026: 27.

539 papers.

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  1. 2022TensoRF: Tensorial Radiance Fieldscited by 1,275
  2. 2021Diffusion Probabilistic Models for 3D Point Cloud Generationcited by 734
  3. 2022Point-E: A System for Generating 3D Point Clouds from Complex Promptscited by 694
  4. 2023One-2-3-45: Any Single Image to 3D Mesh in 45 Seconds without Per-Shape Optimizationcited by 555
  5. 2022LION: Latent Point Diffusion Models for 3D Shape Generationcited by 552
  6. 2021Neural Fields in Visual Computing and Beyondcited by 539
  7. 20213D Shape Generation and Completion through Point-Voxel Diffusioncited by 516
  8. 2021Deep Marching Tetrahedra: a Hybrid Representation for High-Resolution 3D Shape Synthesiscited by 459
  9. 2021ABO: Dataset and Benchmarks for Real-World 3D Object Understandingcited by 360
  10. 2021StyleSDF: High-Resolution 3D-Consistent Image and Geometry Generationcited by 289
  11. 2021PoinTr: Diverse Point Cloud Completion with Geometry-Aware Transformerscited by 284
  12. 2023One-2-3-45++: Fast Single Image to 3D Objects with Consistent Multi-View Generation and 3D Diffusioncited by 283
38 more
  1. 2021DeepCAD: A Deep Generative Network for Computer-Aided Design Modelscited by 266
  2. 2023MeshGPT: Generating Triangle Meshes with Decoder-Only Transformerscited by 254
  3. 2024TripoSR: Fast 3D Object Reconstruction from a Single Imagecited by 227
  4. 2021Editing Conditional Radiance Fieldscited by 222
  5. 2021Self-Supervised Pretraining of 3D Features on any Point-Cloudcited by 217
  6. 2021Shape As Points: A Differentiable Poisson Solvercited by 186
  7. 2021CodeNeRF: Disentangled Neural Radiance Fields for Object Categoriescited by 183
  8. 2021SnowflakeNet: Point Cloud Completion by Snowflake Point Deconvolution with Skip-Transformercited by 162
  9. 2022NeuMesh: Learning Disentangled Neural Mesh-based Implicit Field for Geometry and Texture Editingcited by 141
  10. 2023Flexible Isosurface Extraction for Gradient-Based Mesh Optimizationcited by 138
  11. 2021Layered Neural Atlases for Consistent Video Editingcited by 137
  12. 2021NeRS: Neural Reflectance Surfaces for Sparse-view 3D Reconstruction in the Wildcited by 136
  13. 2022D2NeRF: Self-Supervised Decoupling of Dynamic and Static Objects from a Monocular Videocited by 136
  14. 2022Diffusion-SDF: Conditional Generative Modeling of Signed Distance Functionscited by 133
  15. 2022Neural Wavelet-domain Diffusion for 3D Shape Generationcited by 132
  16. 2022Diffusion-SDF: Text-to-Shape via Voxelized Diffusioncited by 128
  17. 2021NeSF: Neural Semantic Fields for Generalizable Semantic Segmentation of 3D Scenescited by 119
  18. 2021Capturing, Reconstructing, and Simulating: the UrbanScene3D Datasetcited by 117
  19. 2023S-NeRF: Neural Radiance Fields for Street Viewscited by 116
  20. 2021Im2Vec: Synthesizing Vector Graphics without Vector Supervisioncited by 114
  21. 2023DiT-3D: Exploring Plain Diffusion Transformers for 3D Shape Generationcited by 110
  22. 2024RadSplat: Radiance Field-Informed Gaussian Splatting for Robust Real-Time Rendering with 900+ FPScited by 110
  23. 2022ShapeFormer: Transformer-based Shape Completion via Sparse Representationcited by 106
  24. 2023HoloDiffusion: Training a 3D Diffusion Model using 2D Imagescited by 106
  25. 2021Variational Relational Point Completion Networkcited by 103
  26. 2021Stylizing 3D Scene via Implicit Representation and HyperNetworkcited by 103
  27. 2021Deep Generative Models in Engineering Design: A Reviewcited by 103
  28. 2023Neural Kernel Surface Reconstructioncited by 102
  29. 2021Neural Parts: Learning Expressive 3D Shape Abstractions with Invertible Neural Networkscited by 101
  30. 2021gradSim: Differentiable simulation for system identification and visuomotor controlcited by 100
  31. 2021NeuTex: Neural Texture Mapping for Volumetric Neural Renderingcited by 98
  32. 2021Holistic 3D Scene Understanding from a Single Image with Implicit Representationcited by 94
  33. 2022SDF-StyleGAN: Implicit SDF-Based StyleGAN for 3D Shape Generationcited by 92
  34. 2022POCO: Point Convolution for Surface Reconstructioncited by 91
  35. 2021The Power of Points for Modeling Humans in Clothingcited by 90
  36. 2022What's in your hands? 3D Reconstruction of Generic Objects in Handscited by 90
  37. 2022ReLU Fields: The Little Non-linearity That Couldcited by 87
  38. 2021Graph-to-3D: End-to-End Generation and Manipulation of 3D Scenes Using Scene Graphscited by 83

50 most cited of 539 All 539 (JSON)