mlsanityData to 14 Oct 2026

3D Point Cloud Generative Adversarial Network Based on Tree Structured Graph Convolutions

arXiv 1905.06292 ↗First posted 15 May 2019cs.CVcited by 173

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Cited by 43 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

In this paper, we propose a novel generative adversarial network (GAN) for 3D point clouds generation, which is called tree-GAN. To achieve state-of-the-art performance for multi-class 3D point cloud generation, a tree-structured graph convolution network (TreeGCN) is introduced as a generator for tree-GAN. Because TreeGCN performs graph convolutions within a tree, it can use ancestor information to boost the representation power for features. To evaluate GANs for 3D point clouds accurately, we develop a novel evaluation metric called Frechet point cloud distance (FPD). Experimental results demonstrate that the proposed tree-GAN outperforms state-of-the-art GANs in terms of both conventional metrics and FPD, and can generate point clouds for different semantic parts without prior knowledge.

Key facts. Cited by 173 papers in the map (data through 14 Oct 2026). First posted on arXiv 15 May 2019.

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Citing papers per year: 2021: 33; 2022: 37; 2023: 47; 2024: 27; 2025: 25; 2026: 4.

173 papers.

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  1. 2021Diffusion Probabilistic Models for 3D Point Cloud Generationcited by 735
  2. 2022LION: Latent Point Diffusion Models for 3D Shape Generationcited by 553
  3. 20213D Shape Generation and Completion through Point-Voxel Diffusioncited by 516
  4. 2023MeshGPT: Generating Triangle Meshes with Decoder-Only Transformerscited by 259
  5. 2023LucidDreamer: Domain-free Generation of 3D Gaussian Splatting Scenescited by 231
  6. 2022Dream3D: Zero-Shot Text-to-3D Synthesis Using 3D Shape Prior and Text-to-Image Diffusion Modelscited by 151
  7. 2024Lumina-Next: Making Lumina-T2X Stronger and Faster with Next-DiTcited by 132
  8. 20234DGen: Grounded 4D Content Generation with Spatial-temporal Consistencycited by 117
  9. 2023DiT-3D: Exploring Plain Diffusion Transformers for 3D Shape Generationcited by 110
  10. 2022ShapeFormer: Transformer-based Shape Completion via Sparse Representationcited by 106
  11. 2022Learning to Generate Realistic LiDAR Point Cloudscited by 93
  12. 2022SDF-StyleGAN: Implicit SDF-Based StyleGAN for 3D Shape Generationcited by 92
38 more
  1. 2023TexFusion: Synthesizing 3D Textures with Text-Guided Image Diffusion Modelscited by 87
  2. 2021Unsupervised 3D Shape Completion through GAN Inversioncited by 70
  3. 2022Towards Implicit Text-Guided 3D Shape Generationcited by 68
  4. 20223D-LDM: Neural Implicit 3D Shape Generation with Latent Diffusion Modelscited by 67
  5. 20253D and 4D World Modeling: A Surveycited by 67
  6. 2023SALAD: Part-Level Latent Diffusion for 3D Shape Generation and Manipulationcited by 66
  7. 2024Advances in 3D Generation: A Surveycited by 63
  8. 2022A Survey on Graph Neural Networks and Graph Transformers in Computer Vision: A Task-Oriented Perspectivecited by 56
  9. 2022Fast Point Cloud Generation with Straight Flowscited by 56
  10. 2023LiDAR Data Synthesis with Denoising Diffusion Probabilistic Modelscited by 55
  11. 2021A Review of Location Encoding for GeoAI: Methods and Applicationscited by 54
  12. 2024Dyn-HaMR: Recovering 4D Interacting Hand Motion from a Dynamic Cameracited by 51
  13. 2021Point-set Distances for Learning Representations of 3D Point Cloudscited by 48
  14. 20213D Shape Generation with Grid-based Implicit Functionscited by 48
  15. 20233D generation on ImageNetcited by 47
  16. 2024RangeLDM: Fast Realistic LiDAR Point Cloud Generationcited by 47
  17. 2021Particle Cloud Generation with Message Passing Generative Adversarial Networkscited by 46
  18. 2021Style-based Point Generator with Adversarial Rendering for Point Cloud Completioncited by 45
  19. 2023Controllable Mesh Generation Through Sparse Latent Point Diffusion Modelscited by 41
  20. 2022Snowflake Point Deconvolution for Point Cloud Completion and Generation with Skip-Transformercited by 38
  21. 2021DeepMetaHandles: Learning Deformation Meta-Handles of 3D Meshes with Biharmonic Coordinatescited by 37
  22. 2021RGL-NET: A Recurrent Graph Learning framework for Progressive Part Assemblycited by 36
  23. 2022Text and Image Guided 3D Avatar Generation and Manipulationcited by 35
  24. 2022PointDistiller: Structured Knowledge Distillation Towards Efficient and Compact 3D Detectioncited by 33
  25. 2021Voxel-based Network for Shape Completion by Leveraging Edge Generationcited by 32
  26. 2023TAPS3D: Text-Guided 3D Textured Shape Generation from Pseudo Supervisioncited by 28
  27. 2024InterHandGen: Two-Hand Interaction Generation via Cascaded Reverse Diffusioncited by 28
  28. 20223PSDF: Three-Pole Signed Distance Function for Learning Surfaces with Arbitrary Topologiescited by 27
  29. 2023Multi-modal Machine Learning in Engineering Design: A Review and Future Directionscited by 27
  30. 2022SurfGen: Adversarial 3D Shape Synthesis with Explicit Surface Discriminatorscited by 26
  31. 2021OctField: Hierarchical Implicit Functions for 3D Modelingcited by 25
  32. 20233DQD: Generalized Deep 3D Shape Prior via Part-Discretized Diffusion Processcited by 25
  33. 2023GECCO: Geometrically-Conditioned Point Diffusion Modelscited by 24
  34. 2023Advancements in Point Cloud Data Augmentation for Deep Learning: A Surveycited by 24
  35. 2024HoloDreamer: Holistic 3D Panoramic World Generation from Text Descriptionscited by 24
  36. 2024SceneDreamer360: Text-Driven 3D-Consistent Scene Generation with Panoramic Gaussian Splattingcited by 24
  37. 2021Learning to Generate 3D Shapes with Generative Cellular Automatacited by 22
  38. 2022CSDN: Cross-modal Shape-transfer Dual-refinement Network for Point Cloud Completioncited by 22

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