mlsanityData to 7 Oct 2026

Learning Implicit Fields for Generative Shape Modeling

arXiv 1812.02822 ↗First posted 6 Dec 2018cs.GR cs.CV cs.LGcited by 1,060

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Cited by 71 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

We advocate the use of implicit fields for learning generative models of shapes and introduce an implicit field decoder, called IM-NET, for shape generation, aimed at improving the visual quality of the generated shapes. An implicit field assigns a value to each point in 3D space, so that a shape can be extracted as an iso-surface. IM-NET is trained to perform this assignment by means of a binary classifier. Specifically, it takes a point coordinate, along with a feature vector encoding a shape, and outputs a value which indicates whether the point is outside the shape or not. By replacing conventional decoders by our implicit decoder for representation learning (via IM-AE) and shape generation (via IM-GAN), we demonstrate superior results for tasks such as generative shape modeling, interpolation, and single-view 3D reconstruction, particularly in terms of visual quality. Code and supplementary material are available at https://github.com/czq142857/implicit-decoder.

Key facts. Cited by 1,060 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Dec 2018.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 192; 2022: 265; 2023: 284; 2024: 173; 2025: 95; 2026: 51.

1,060 papers.

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  1. 2021NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstructioncited by 1,701
  2. 2021Plenoxels: Radiance Fields without Neural Networkscited by 1,607
  3. 2022Magic3D: High-Resolution Text-to-3D Content Creationcited by 1,285
  4. 2021Efficient Geometry-aware 3D Generative Adversarial Networkscited by 1,204
  5. 2021PlenOctrees for Real-time Rendering of Neural Radiance Fieldscited by 904
  6. 2023LRM: Large Reconstruction Model for Single Image to 3Dcited by 834
  7. 2021Depth-supervised NeRF: Fewer Views and Faster Training for Freecited by 732
  8. 2021KiloNeRF: Speeding up Neural Radiance Fields with Thousands of Tiny MLPscited by 672
  9. 2023Mip-Splatting: Alias-free 3D Gaussian Splattingcited by 661
  10. 2021RegNeRF: Regularizing Neural Radiance Fields for View Synthesis from Sparse Inputscited by 614
  11. 2021NICE-SLAM: Neural Implicit Scalable Encoding for SLAMcited by 586
  12. 2022LION: Latent Point Diffusion Models for 3D Shape Generationcited by 552
38 more
  1. 2022PETR: Position Embedding Transformation for Multi-View 3D Object Detectioncited by 550
  2. 2021Neural Fields in Visual Computing and Beyondcited by 539
  3. 2021FastNeRF: High-Fidelity Neural Rendering at 200FPScited by 510
  4. 2022Latent-NeRF for Shape-Guided Generation of 3D Shapes and Texturescited by 506
  5. 2022Point-NeRF: Point-based Neural Radiance Fieldscited by 503
  6. 2022MonoSDF: Exploring Monocular Geometric Cues for Neural Implicit Surface Reconstructioncited by 467
  7. 2021Deep Marching Tetrahedra: a Hybrid Representation for High-Resolution 3D Shape Synthesiscited by 459
  8. 20233DShape2VecSet: A 3D Shape Representation for Neural Fields and Generative Diffusion Modelscited by 449
  9. 2022GET3D: A Generative Model of High Quality 3D Textured Shapes Learned from Imagescited by 446
  10. 2024CLAY: A Controllable Large-scale Generative Model for Creating High-quality 3D Assetscited by 444
  11. 2021Implicit Behavioral Cloningcited by 442
  12. 2024MonST3R: A Simple Approach for Estimating Geometry in the Presence of Motioncited by 431
  13. 2021Neural Geometric Level of Detail: Real-time Rendering with Implicit 3D Shapescited by 426
  14. 2021Dynamic View Synthesis from Dynamic Monocular Videocited by 420
  15. 2021GAN Inversion: A Surveycited by 366
  16. 2021Advances in Neural Renderingcited by 365
  17. 2025Hunyuan3D 2.0: Scaling Diffusion Models for High Resolution Textured 3D Assets Generationcited by 342
  18. 2023OmniObject3D: Large-Vocabulary 3D Object Dataset for Realistic Perception, Reconstruction and Generationcited by 324
  19. 2021StyleSDF: High-Resolution 3D-Consistent Image and Geometry Generationcited by 289
  20. 2022SDFusion: Multimodal 3D Shape Completion, Reconstruction, and Generationcited by 287
  21. 2021ICON: Implicit Clothed humans Obtained from Normalscited by 283
  22. 20223D Neural Field Generation using Triplane Diffusioncited by 274
  23. 2021DeepCAD: A Deep Generative Network for Computer-Aided Design Modelscited by 266
  24. 2021CLIP-Forge: Towards Zero-Shot Text-to-Shape Generationcited by 254
  25. 2023MeshGPT: Generating Triangle Meshes with Decoder-Only Transformerscited by 254
  26. 2023Text2Tex: Text-driven Texture Synthesis via Diffusion Modelscited by 234
  27. 2021COIN: COmpression with Implicit Neural representationscited by 232
  28. 2021I M Avatar: Implicit Morphable Head Avatars from Videoscited by 230
  29. 2021Editing Conditional Radiance Fieldscited by 222
  30. 2021SNARF: Differentiable Forward Skinning for Animating Non-Rigid Neural Implicit Shapescited by 221
  31. 2021HyperNeRF: A Higher-Dimensional Representation for Topologically Varying Neural Radiance Fieldscited by 210
  32. 2021Neural-PIL: Neural Pre-Integrated Lighting for Reflectance Decompositioncited by 204
  33. 2021Neural Descriptor Fields: SE(3)-Equivariant Object Representations for Manipulationcited by 204
  34. 2023MeshDiffusion: Score-based Generative 3D Mesh Modelingcited by 198
  35. 2021Neural Articulated Radiance Fieldcited by 196
  36. 2021NerfingMVS: Guided Optimization of Neural Radiance Fields for Indoor Multi-view Stereocited by 191
  37. 2023DreamGaussian4D: Generative 4D Gaussian Splattingcited by 191
  38. 2021Stereo Radiance Fields (SRF): Learning View Synthesis for Sparse Views of Novel Scenescited by 190

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