mlsanityData to 7 Oct 2026

Neural Wavelet-domain Diffusion for 3D Shape Generation

arXiv 2209.08725 ↗First posted 19 Sep 2022cs.CV cs.GRcited by 132

Not on the map. Why?

Cited by 55 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

This paper presents a new approach for 3D shape generation, enabling direct generative modeling on a continuous implicit representation in wavelet domain. Specifically, we propose a compact wavelet representation with a pair of coarse and detail coefficient volumes to implicitly represent 3D shapes via truncated signed distance functions and multi-scale biorthogonal wavelets, and formulate a pair of neural networks: a generator based on the diffusion model to produce diverse shapes in the form of coarse coefficient volumes; and a detail predictor to further produce compatible detail coefficient volumes for enriching the generated shapes with fine structures and details. Both quantitative and qualitative experimental results manifest the superiority of our approach in generating diverse and high-quality shapes with complex topology and structures, clean surfaces, and fine details, exceeding the 3D generation capabilities of the state-of-the-art models.

Key facts. Cited by 132 papers in the map (data through 7 Oct 2026). Builds on 47 of its 60 references that are in the map. First posted on arXiv 19 Sep 2022.

Builds on

47 of 60 references.

References · 47

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  3. 2020Denoising Diffusion Implicit Modelscited by 9,655
  4. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
  5. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,005
  6. 2021Improved Denoising Diffusion Probabilistic Modelscited by 3,736
  7. 2015ShapeNet: An Information-Rich 3D Model Repositorycited by 2,880
  8. 2019DeepSDF: Learning Continuous Signed Distance Functions for Shape Representationcited by 2,682
  9. 2018Occupancy Networks: Learning 3D Reconstruction in Function Spacecited by 2,037
  10. 2018Learning Implicit Fields for Generative Shape Modelingcited by 1,060
  11. 2016A Point Set Generation Network for 3D Object Reconstruction from a Single Imagecited by 972
  12. 2020Neural Sparse Voxel Fieldscited by 959
35 more
  1. 2021Diffusion Probabilistic Models for 3D Point Cloud Generationcited by 734
  2. 2020Implicit Geometric Regularization for Learning Shapescited by 707
  3. 2019Differentiable Volumetric Rendering: Learning Implicit 3D Representations without 3D Supervisioncited by 699
  4. 2018Pixel2Mesh: Generating 3D Mesh Models from Single RGB Imagescited by 649
  5. 2016Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modelingcited by 558
  6. 2018AtlasNet: A Papier-Mach'e Approach to Learning 3D Surface Generationcited by 539
  7. 20213D Shape Generation and Completion through Point-Voxel Diffusioncited by 516
  8. 2019PointFlow: 3D Point Cloud Generation with Continuous Normalizing Flowscited by 487
  9. 2021Neural Geometric Level of Detail: Real-time Rendering with Implicit 3D Shapescited by 426
  10. 2019SAL: Sign Agnostic Learning of Shapes from Raw Datacited by 389
  11. 2020Implicit Functions in Feature Space for 3D Shape Reconstruction and Completioncited by 343
  12. 2019DISN: Deep Implicit Surface Network for High-quality Single-view 3D Reconstructioncited by 332
  13. 2020Learning Gradient Fields for Shape Generationcited by 245
  14. 2016Learning a Predictable and Generative Vector Representation for Objectscited by 173
  15. 2022ShapeFormer: Transformer-based Shape Completion via Sparse Representationcited by 106
  16. 2019Learning to Infer Implicit Surfaces without 3D Supervisioncited by 103
  17. 2020SoftFlow: Probabilistic Framework for Normalizing Flow on Manifoldscited by 97
  18. 2022SDF-StyleGAN: Implicit SDF-Based StyleGAN for 3D Shape Generationcited by 92
  19. 2020DualSDF: Semantic Shape Manipulation using a Two-Level Representationcited by 82
  20. 2021Deep Implicit Moving Least-Squares Functions for 3D Reconstructioncited by 67
  21. 2017Improved Adversarial Systems for 3D Object Generation and Reconstructioncited by 66
  22. 20213D Shape Generation with Grid-based Implicit Functionscited by 48
  23. 2020Adversarial Generation of Continuous Implicit Shape Representationscited by 46
  24. 2020Progressive Point Cloud Deconvolution Generation Networkcited by 46
  25. 2020D2IM-Net: Learning Detail Disentangled Implicit Fields from Single Imagescited by 42
  26. 2019A Skeleton-bridged Deep Learning Approach for Generating Meshes of Complex Topologies from Single RGB Imagescited by 37
  27. 2021Multiresolution Deep Implicit Functions for 3D Shape Representationcited by 35
  28. 2020SkeletonNet: A Topology-Preserving Solution for Learning Mesh Reconstruction of Object Surfaces from RGB Imagescited by 30
  29. 2020Sign-Agnostic Implicit Learning of Surface Self-Similarities for Shape Modeling and Reconstruction from Raw Point Cloudscited by 30
  30. 2019GEOMetrics: Exploiting Geometric Structure for Graph-Encoded Objectscited by 27
  31. 2022SurfGen: Adversarial 3D Shape Synthesis with Explicit Surface Discriminatorscited by 26
  32. 2020Ladybird: Quasi-Monte Carlo Sampling for Deep Implicit Field Based 3D Reconstruction with Symmetrycited by 24
  33. 2017Rethinking Reprojection: Closing the Loop for Pose-aware ShapeReconstruction from a Single Imagecited by 20
  34. 2022MINER: Multiscale Implicit Neural Representationscited by 20
  35. 2020MRGAN: Multi-Rooted 3D Shape Generation with Unsupervised Part Disentanglementcited by 15

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 6; 2023: 42; 2024: 38; 2025: 31; 2026: 15.

132 papers.

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  1. 2024Structured 3D Latents for Scalable and Versatile 3D Generationcited by 742
  2. 20233DShape2VecSet: A 3D Shape Representation for Neural Fields and Generative Diffusion Modelscited by 449
  3. 2023MeshGPT: Generating Triangle Meshes with Decoder-Only Transformerscited by 254
  4. 2023XCube: Large-Scale 3D Generative Modeling using Sparse Voxel Hierarchiescited by 183
  5. 2022RenderDiffusion: Image Diffusion for 3D Reconstruction, Inpainting and Generationcited by 162
  6. 2023DiffuScene: Denoising Diffusion Models for Generative Indoor Scene Synthesiscited by 159
  7. 2025Native and Compact Structured Latents for 3D Generationcited by 157
  8. 2022Diffusion-SDF: Conditional Generative Modeling of Signed Distance Functionscited by 133
  9. 2023Locally Attentional SDF Diffusion for Controllable 3D Shape Generationcited by 129
  10. 2023State of the Art on Diffusion Models for Visual Computingcited by 126
  11. 2024CraftsMan3D: High-fidelity Mesh Generation with 3D Native Generation and Interactive Geometry Refinercited by 120
  12. 2023HoloDiffusion: Training a 3D Diffusion Model using 2D Imagescited by 106
38 more
  1. 2024BrepGen: A B-rep Generative Diffusion Model with Structured Latent Geometrycited by 101
  2. 2023Score-based Diffusion Models in Function Spacecited by 80
  3. 2025SparseFlex: High-Resolution and Arbitrary-Topology 3D Shape Modelingcited by 76
  4. 2023LooseControl: Lifting ControlNet for Generalized Depth Conditioningcited by 68
  5. 20223D-LDM: Neural Implicit 3D Shape Generation with Latent Diffusion Modelscited by 67
  6. 2023SALAD: Part-Level Latent Diffusion for 3D Shape Generation and Manipulationcited by 66
  7. 2023InstructEdit: Improving Automatic Masks for Diffusion-based Image Editing With User Instructionscited by 64
  8. 2023Texture Generation on 3D Meshes with Point-UV Diffusioncited by 63
  9. 2024Advances in 3D Generation: A Surveycited by 62
  10. 2025OmniPart: Part-Aware 3D Generation with Semantic Decoupling and Structural Cohesioncited by 60
  11. 2025HoloPart: Generative 3D Part Amodal Segmentationcited by 57
  12. 2022Efficient Diffusion Models for Vision: A Surveycited by 50
  13. 2025SceneGen: Single-Image 3D Scene Generation in One Feedforward Passcited by 49
  14. 2023Mosaic-SDF for 3D Generative Modelscited by 48
  15. 2023RenAIssance: A Survey into AI Text-to-Image Generation in the Era of Large Modelcited by 47
  16. 2023UniDream: Unifying Diffusion Priors for Relightable Text-to-3D Generationcited by 47
  17. 2024PivotMesh: Generic 3D Mesh Generation via Pivot Vertices Guidancecited by 47
  18. 2023WaveDM: Wavelet-Based Diffusion Models for Image Restorationcited by 46
  19. 2024RangeLDM: Fast Realistic LiDAR Point Cloud Generationcited by 45
  20. 2024OctFusion: Octree-based Diffusion Models for 3D Shape Generationcited by 40
  21. 2023DiffFacto: Controllable Part-Based 3D Point Cloud Generation with Cross Diffusioncited by 32
  22. 2025AToken: A Unified Tokenizer for Visioncited by 30
  23. 2025ReconViaGen: Towards Accurate Multi-view 3D Object Reconstruction via Generationcited by 30
  24. 2024CharacterGen: Efficient 3D Character Generation from Single Images with Multi-View Pose Canonicalizationcited by 28
  25. 2024Wavelet Diffusion Neural Operatorcited by 28
  26. 2023Neural Wavelet-domain Diffusion for 3D Shape Generation, Inversion, and Manipulationcited by 27
  27. 2025OctGPT: Octree-based Multiscale Autoregressive Models for 3D Shape Generationcited by 27
  28. 20233DQD: Generalized Deep 3D Shape Prior via Part-Discretized Diffusion Processcited by 25
  29. 2023NAP: Neural 3D Articulation Priorcited by 25
  30. 2024Make-A-Shape: a Ten-Million-scale 3D Shape Modelcited by 25
  31. 2023DiffInDScene: Diffusion-based High-Quality 3D Indoor Scene Generationcited by 23
  32. 2023HumanLiff: Layer-wise 3D Human Generation with Diffusion Modelcited by 23
  33. 2024UDiFF: Generating Conditional Unsigned Distance Fields with Optimal Wavelet Diffusioncited by 23
  34. 2023PI3D: Efficient Text-to-3D Generation with Pseudo-Image Diffusioncited by 20
  35. 2023VecFusion: Vector Font Generation with Diffusioncited by 20
  36. 2025DTGBrepGen: A Novel B-rep Generative Model through Decoupling Topology and Geometrycited by 20
  37. 2025FunDiff: Diffusion Models over Function Spaces for Physics-Informed Generative Modelingcited by 19
  38. 2023EXIM: A Hybrid Explicit-Implicit Representation for Text-Guided 3D Shape Generationcited by 16

50 most cited of 132 All 132 (JSON)

Similar, not cited

Close in wording, no citation either way

  1. 2024Part-aware Shape Generation with Latent 3D Diffusion of Neural Voxel Fields
  2. 2024Topology-Aware Latent Diffusion for 3D Shape Generation
  3. 2024An Object is Worth 64x64 Pixels: Generating 3D Object via Image Diffusion
  4. 20223D Neural Field Generation using Triplane Diffusion
  5. 2023Single Mesh Diffusion Models with Field Latents for Texture Generation
  6. 2026Rethinking 3D Shape Generation: Diffusion over Superquadrics
  7. 2023Few-shot 3D Shape Generation
  8. 2023Sin3DM: Learning a Diffusion Model from a Single 3D Textured Shape