mlsanityData to 7 Oct 2026

Multi-chart Generative Surface Modeling

arXiv 1806.02143 ↗First posted 6 Jun 2018cs.CVcited by 26

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Abstract (arXiv)

This paper introduces a 3D shape generative model based on deep neural networks. A new image-like (i.e., tensor) data representation for genus-zero 3D shapes is devised. It is based on the observation that complicated shapes can be well represented by multiple parameterizations (charts), each focusing on a different part of the shape. The new tensor data representation is used as input to Generative Adversarial Networks for the task of 3D shape generation. The 3D shape tensor representation is based on a multi-chart structure that enjoys a shape covering property and scale-translation rigidity. Scale-translation rigidity facilitates high quality 3D shape learning and guarantees unique reconstruction. The multi-chart structure uses as input a dataset of 3D shapes (with arbitrary connectivity) and a sparse correspondence between them. The output of our algorithm is a generative model that learns the shape distribution and is able to generate novel shapes, interpolate shapes, and explore the generated shape space. The effectiveness of the method is demonstrated for the task of anatomic shape generation including human body and bone (teeth) shape generation.

Key facts. Cited by 26 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Jun 2018.

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Citing papers per year: 2021: 6; 2022: 6; 2023: 9; 2024: 3; 2025: 1; 2026: 1.

26 papers.

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  1. 2024Advances in 3D Generation: A Surveycited by 62
  2. 2021ARAPReg: An As-Rigid-As Possible Regularization Loss for Learning Deformable Shape Generatorscited by 40
  3. 2021Neural Surface Mapscited by 34
  4. 2022Neural Template: Topology-aware Reconstruction and Disentangled Generation of 3D Meshescited by 34
  5. 2024An Object is Worth 64x64 Pixels: Generating 3D Object via Image Diffusioncited by 34
  6. 2022Learning to Generate 3D Shapes from a Single Examplecited by 31
  7. 2023Multi-modal Machine Learning in Engineering Design: A Review and Future Directionscited by 27
  8. 2021Go with the Flows: Mixtures of Normalizing Flows for Point Cloud Generation and Reconstructioncited by 16
  9. 2023Surrogate Modeling of Car Drag Coefficient with Depth and Normal Renderingscited by 15
  10. 2022FakeNews: GAN-based generation of realistic 3D volumetric data -- A systematic review and taxonomycited by 13
  11. 2023MV-DeepSDF: Implicit Modeling with Multi-Sweep Point Clouds for 3D Vehicle Reconstruction in Autonomous Drivingcited by 11
  12. 2021Deep Generative Model for Efficient 3D Airfoil Parameterization and Generationcited by 10
14 more
  1. 2023Robust One-Class Classification with Signed Distance Function using 1-Lipschitz Neural Networkscited by 10
  2. 20223inGAN: Learning a 3D Generative Model from Images of a Self-similar Scenecited by 9
  3. 2023MVControl: Adding Conditional Control to Multi-view Diffusion for Controllable Text-to-3D Generationcited by 8
  4. 2023ET3D: Efficient Text-to-3D Generation via Multi-View Distillationcited by 6
  5. 2021Range-GAN: Range-Constrained Generative Adversarial Network for Conditioned Design Synthesiscited by 5
  6. 2021Learning Mesh Representations via Binary Space Partitioning Tree Networkscited by 5
  7. 2022Neural Rendering of Humans in Novel View and Pose from Monocular Videocited by 4
  8. 2023Test-Time Augmentation for 3D Point Cloud Classification and Segmentationcited by 4
  9. 2023Learning Effective NeRFs and SDFs Representations with 3D Generative Adversarial Networks for 3D Object Generationcited by 3
  10. 2024Optimizing 3D Geometry Reconstruction from Implicit Neural Representationscited by 2
  11. 2022Deep Generative Models on 3D Representations: A Surveycited by 1
  12. 2023Towards AI-Architecture Liberty: A Comprehensive Survey on Design and Generation of Virtual Architecture by Deep Learningcited by 0
  13. 20253D Shape Generation: A Surveycited by 0
  14. 2026Recent Advances and Trends in Learning-based 3D Representationscited by 0