mlsanityData to 14 Oct 2026

Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling

arXiv 1610.07584 ↗First posted 24 Oct 2016cs.CV cs.LGcited by 558

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Cited by 82 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

We study the problem of 3D object generation. We propose a novel framework, namely 3D Generative Adversarial Network (3D-GAN), which generates 3D objects from a probabilistic space by leveraging recent advances in volumetric convolutional networks and generative adversarial nets. The benefits of our model are three-fold: first, the use of an adversarial criterion, instead of traditional heuristic criteria, enables the generator to capture object structure implicitly and to synthesize high-quality 3D objects; second, the generator establishes a mapping from a low-dimensional probabilistic space to the space of 3D objects, so that we can sample objects without a reference image or CAD models, and explore the 3D object manifold; third, the adversarial discriminator provides a powerful 3D shape descriptor which, learned without supervision, has wide applications in 3D object recognition. Experiments demonstrate that our method generates high-quality 3D objects, and our unsupervisedly learned features achieve impressive performance on 3D object recognition, comparable with those of supervised learning methods.

Key facts. Cited by 558 papers in the map (data through 14 Oct 2026). First posted on arXiv 24 Oct 2016.

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Citing papers per year: 2021: 100; 2022: 118; 2023: 140; 2024: 96; 2025: 64; 2026: 40.

558 papers.

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  1. 2022DreamFusion: Text-to-3D using 2D Diffusioncited by 3,025
  2. 2022Magic3D: High-Resolution Text-to-3D Content Creationcited by 1,290
  3. 2021Efficient Geometry-aware 3D Generative Adversarial Networkscited by 1,205
  4. 2024Structured 3D Latents for Scalable and Versatile 3D Generationcited by 770
  5. 2021Common Objects in 3D: Large-Scale Learning and Evaluation of Real-life 3D Category Reconstructioncited by 652
  6. 2022Score Jacobian Chaining: Lifting Pretrained 2D Diffusion Models for 3D Generationcited by 597
  7. 2022LION: Latent Point Diffusion Models for 3D Shape Generationcited by 553
  8. 20213D Shape Generation and Completion through Point-Voxel Diffusioncited by 516
  9. 2021Zero-Shot Text-Guided Object Generation with Dream Fieldscited by 513
  10. 2021Deep Marching Tetrahedra: a Hybrid Representation for High-Resolution 3D Shape Synthesiscited by 460
  11. 20233DShape2VecSet: A 3D Shape Representation for Neural Fields and Generative Diffusion Modelscited by 458
  12. 2022GET3D: A Generative Model of High Quality 3D Textured Shapes Learned from Imagescited by 446
38 more
  1. 2023Instant3D: Fast Text-to-3D with Sparse-View Generation and Large Reconstruction Modelcited by 378
  2. 2021MonoScene: Monocular 3D Semantic Scene Completioncited by 363
  3. 2025Hunyuan3D 2.0: Scaling Diffusion Models for High Resolution Textured 3D Assets Generationcited by 356
  4. 2023OmniObject3D: Large-Vocabulary 3D Object Dataset for Realistic Perception, Reconstruction and Generationcited by 326
  5. 2021Neural RGB-D Surface Reconstructioncited by 313
  6. 2022Rodin: A Generative Model for Sculpting 3D Digital Avatars Using Diffusioncited by 309
  7. 2023RealFusion: 360deg Reconstruction of Any Object from a Single Imagecited by 306
  8. 2021StyleSDF: High-Resolution 3D-Consistent Image and Geometry Generationcited by 289
  9. 2021Mixture of Volumetric Primitives for Efficient Neural Renderingcited by 277
  10. 20223D Neural Field Generation using Triplane Diffusioncited by 274
  11. 2021DeepCAD: A Deep Generative Network for Computer-Aided Design Modelscited by 271
  12. 2023Holodeck: Language Guided Generation of 3D Embodied AI Environmentscited by 268
  13. 2022Point-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-trainingcited by 264
  14. 2023MeshGPT: Generating Triangle Meshes with Decoder-Only Transformerscited by 259
  15. 2021CLIP-Forge: Towards Zero-Shot Text-to-Shape Generationcited by 254
  16. 2023LucidDreamer: Domain-free Generation of 3D Gaussian Splatting Scenescited by 231
  17. 2023Michelangelo: Conditional 3D Shape Generation based on Shape-Image-Text Aligned Latent Representationcited by 228
  18. 2022DiffRF: Rendering-Guided 3D Radiance Field Diffusioncited by 204
  19. 2023MeshDiffusion: Score-based Generative 3D Mesh Modelingcited by 199
  20. 2022CrossPoint: Self-Supervised Cross-Modal Contrastive Learning for 3D Point Cloud Understandingcited by 198
  21. 2023XCube: Large-Scale 3D Generative Modeling using Sparse Voxel Hierarchiescited by 189
  22. 2021Shape As Points: A Differentiable Poisson Solvercited by 187
  23. 2021CodeNeRF: Disentangled Neural Radiance Fields for Object Categoriescited by 183
  24. 2021Spatio-temporal Self-Supervised Representation Learning for 3D Point Cloudscited by 180
  25. 2021Walk in the Cloud: Learning Curves for Point Clouds Shape Analysiscited by 172
  26. 2023RichDreamer: A Generalizable Normal-Depth Diffusion Model for Detail Richness in Text-to-3Dcited by 168
  27. 2023DiffuScene: Denoising Diffusion Models for Generative Indoor Scene Synthesiscited by 166
  28. 2023DreamAvatar: Text-and-Shape Guided 3D Human Avatar Generation via Diffusion Modelscited by 154
  29. 2022Learning 3D Representations from 2D Pre-trained Models via Image-to-Point Masked Autoencoderscited by 144
  30. 2024MeshLRM: Large Reconstruction Model for High-Quality Meshescited by 139
  31. 2022Neural Wavelet-domain Diffusion for 3D Shape Generationcited by 132
  32. 2023Locally Attentional SDF Diffusion for Controllable 3D Shape Generationcited by 130
  33. 2022Diffusion-SDF: Text-to-Shape via Voxelized Diffusioncited by 128
  34. 2025Hunyuan3D 2.5: Towards High-Fidelity 3D Assets Generation with Ultimate Detailscited by 125
  35. 2022Next3D: Generative Neural Texture Rasterization for 3D-Aware Head Avatarscited by 124
  36. 2023Text2NeRF: Text-Driven 3D Scene Generation with Neural Radiance Fieldscited by 123
  37. 2024MeshAnything V2: Artist-Created Mesh Generation With Adjacent Mesh Tokenizationcited by 104
  38. 2021Neural Parts: Learning Expressive 3D Shape Abstractions with Invertible Neural Networkscited by 103

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