mlsanityData to 14 Oct 2026

Learning Shape Templates with Structured Implicit Functions

arXiv 1904.06447 ↗First posted 12 Apr 2019cs.CV cs.GRcited by 242

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Abstract (arXiv)

Template 3D shapes are useful for many tasks in graphics and vision, including fitting observation data, analyzing shape collections, and transferring shape attributes. Because of the variety of geometry and topology of real-world shapes, previous methods generally use a library of hand-made templates. In this paper, we investigate learning a general shape template from data. To allow for widely varying geometry and topology, we choose an implicit surface representation based on composition of local shape elements. While long known to computer graphics, this representation has not yet been explored in the context of machine learning for vision. We show that structured implicit functions are suitable for learning and allow a network to smoothly and simultaneously fit multiple classes of shapes. The learned shape template supports applications such as shape exploration, correspondence, abstraction, interpolation, and semantic segmentation from an RGB image.

Key facts. Cited by 242 papers in the map (data through 14 Oct 2026). First posted on arXiv 12 Apr 2019.

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Citing papers per year: 2021: 72; 2022: 68; 2023: 43; 2024: 29; 2025: 23; 2026: 7.

242 papers.

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  1. 2021Volume Rendering of Neural Implicit Surfacescited by 996
  2. 2021Common Objects in 3D: Large-Scale Learning and Evaluation of Real-life 3D Category Reconstructioncited by 652
  3. 2021UNISURF: Unifying Neural Implicit Surfaces and Radiance Fields for Multi-View Reconstructioncited by 646
  4. 2021RegNeRF: Regularizing Neural Radiance Fields for View Synthesis from Sparse Inputscited by 615
  5. 2021Dynamic View Synthesis from Dynamic Monocular Videocited by 420
  6. 2021Advances in Neural Renderingcited by 365
  7. 2021StyleGAN-V: A Continuous Video Generator with the Price, Image Quality and Perks of StyleGAN2cited by 301
  8. 2021StyleSDF: High-Resolution 3D-Consistent Image and Geometry Generationcited by 289
  9. 20223D Neural Field Generation using Triplane Diffusioncited by 274
  10. 2021NeRV: Neural Representations for Videoscited by 256
  11. 2023LucidDreamer: Domain-free Generation of 3D Gaussian Splatting Scenescited by 231
  12. 2021Neural Articulated Radiance Fieldcited by 196
38 more
  1. 2021SCANimate: Weakly Supervised Learning of Skinned Clothed Avatar Networkscited by 181
  2. 2021NEAT: Neural Attention Fields for End-to-End Autonomous Drivingcited by 174
  3. 2021CIPS-3D: A 3D-Aware Generator of GANs Based on Conditionally-Independent Pixel Synthesiscited by 149
  4. 2021BACON: Band-limited Coordinate Networks for Multiscale Scene Representationcited by 143
  5. 2022D2NeRF: Self-Supervised Decoupling of Dynamic and Static Objects from a Monocular Videocited by 137
  6. 2021MINE: Towards Continuous Depth MPI with NeRF for Novel View Synthesiscited by 133
  7. 2022Generalizable Patch-Based Neural Renderingcited by 122
  8. 2021NeSF: Neural Semantic Fields for Generalizable Semantic Segmentation of 3D Scenescited by 119
  9. 2021Neural Lumigraph Renderingcited by 105
  10. 2021Neural Parts: Learning Expressive 3D Shape Abstractions with Invertible Neural Networkscited by 103
  11. 2021imGHUM: Implicit Generative Models of 3D Human Shape and Articulated Posecited by 103
  12. 2022VideoINR: Learning Video Implicit Neural Representation for Continuous Space-Time Super-Resolutioncited by 101
  13. 2021Holistic 3D Scene Understanding from a Single Image with Implicit Representationcited by 94
  14. 20223DILG: Irregular Latent Grids for 3D Generative Modelingcited by 93
  15. 2022POCO: Point Convolution for Surface Reconstructioncited by 92
  16. 2021NPMs: Neural Parametric Models for 3D Deformable Shapescited by 90
  17. 2022GIRAFFE HD: A High-Resolution 3D-aware Generative Modelcited by 86
  18. 2022GARF: Gaussian Activated Radiance Fields for High Fidelity Reconstruction and Pose Estimationcited by 86
  19. 2021LOLNeRF: Learn from One Lookcited by 84
  20. 2025PartCrafter: Structured 3D Mesh Generation via Compositional Latent Diffusion Transformerscited by 84
  21. 2021Self-Supervised Collision Handling via Generative 3D Garment Models for Virtual Try-Oncited by 81
  22. 2022Transformers as Meta-Learners for Implicit Neural Representationscited by 79
  23. 2021Unsupervised Learning of 3D Object Categories from Videos in the Wildcited by 67
  24. 2022Unified Implicit Neural Stylizationcited by 67
  25. 2023SALAD: Part-Level Latent Diffusion for 3D Shape Generation and Manipulationcited by 66
  26. 2021RGB-D Local Implicit Function for Depth Completion of Transparent Objectscited by 64
  27. 2022ImFace: A Nonlinear 3D Morphable Face Model with Implicit Neural Representationscited by 64
  28. 2022LISA: Learning Implicit Shape and Appearance of Handscited by 63
  29. 2023Deep Learning on Implicit Neural Representations of Shapescited by 63
  30. 2024Advances in 3D Generation: A Surveycited by 63
  31. 2024PartGen: Part-level 3D Generation and Reconstruction with Multi-View Diffusion Modelscited by 62
  32. 2021Geometry-Consistent Neural Shape Representation with Implicit Displacement Fieldscited by 60
  33. 2021SAPE: Spatially-Adaptive Progressive Encoding for Neural Optimizationcited by 59
  34. 2021Neural Fields as Learnable Kernels for 3D Reconstructioncited by 58
  35. 2021UltraSR: Spatial Encoding is a Missing Key for Implicit Image Function-based Arbitrary-Scale Super-Resolutioncited by 57
  36. 2021CAMPARI: Camera-Aware Decomposed Generative Neural Radiance Fieldscited by 55
  37. 2021Implicit Neural Video Compressioncited by 52
  38. 2021Generative Occupancy Fields for 3D Surface-Aware Image Synthesiscited by 51

50 most cited of 242 All 242 (JSON)