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Learning a Predictable and Generative Vector Representation for Objects

arXiv 1603.08637 ↗First posted 29 Mar 2016cs.CVcited by 173

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Abstract (arXiv)

What is a good vector representation of an object? We believe that it should be generative in 3D, in the sense that it can produce new 3D objects; as well as be predictable from 2D, in the sense that it can be perceived from 2D images. We propose a novel architecture, called the TL-embedding network, to learn an embedding space with these properties. The network consists of two components: (a) an autoencoder that ensures the representation is generative; and (b) a convolutional network that ensures the representation is predictable. This enables tackling a number of tasks including voxel prediction from 2D images and 3D model retrieval. Extensive experimental analysis demonstrates the usefulness and versatility of this embedding.

Key facts. Cited by 173 papers in the map (data through 14 Oct 2026). First posted on arXiv 29 Mar 2016.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 49; 2022: 40; 2023: 47; 2024: 23; 2025: 8; 2026: 6.

173 papers.

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  1. 2023Zero-1-to-3: Zero-shot One Image to 3D Objectcited by 1,489
  2. 2023LRM: Large Reconstruction Model for Single Image to 3Dcited by 849
  3. 2021Common Objects in 3D: Large-Scale Learning and Evaluation of Real-life 3D Category Reconstructioncited by 652
  4. 2023One-2-3-45: Any Single Image to 3D Mesh in 45 Seconds without Per-Shape Optimizationcited by 560
  5. 20233DShape2VecSet: A 3D Shape Representation for Neural Fields and Generative Diffusion Modelscited by 458
  6. 2021MonoScene: Monocular 3D Semantic Scene Completioncited by 363
  7. 2021PoinTr: Diverse Point Cloud Completion with Geometry-Aware Transformerscited by 286
  8. 2021DeepCAD: A Deep Generative Network for Computer-Aided Design Modelscited by 271
  9. 2023Triplane Meets Gaussian Splatting: Fast and Generalizable Single-View 3D Reconstruction with Transformerscited by 258
  10. 2025SAM 3D: 3Dfy Anything in Imagescited by 247
  11. 2021Self-Supervised Pretraining of 3D Features on any Point-Cloudcited by 217
  12. 2022SparseFusion: Distilling View-conditioned Diffusion for 3D Reconstructioncited by 216
38 more
  1. 2022AutoSDF: Shape Priors for 3D Completion, Reconstruction and Generationcited by 208
  2. 2023A Survey on Self-supervised Learning: Algorithms, Applications, and Future Trendscited by 207
  3. 2021NeRS: Neural Reflectance Surfaces for Sparse-view 3D Reconstruction in the Wildcited by 136
  4. 2022Neural Wavelet-domain Diffusion for 3D Shape Generationcited by 132
  5. 2021HDR-NeRF: High Dynamic Range Neural Radiance Fieldscited by 114
  6. 2023NeRF-DS: Neural Radiance Fields for Dynamic Specular Objectscited by 111
  7. 2023Neural Kernel Surface Reconstructioncited by 104
  8. 2022What's in your hands? 3D Reconstruction of Generic Objects in Handscited by 90
  9. 2023NDC-Scene: Boost Monocular 3D Semantic Scene Completion in Normalized Device Coordinates Spacecited by 90
  10. 2022Deforming Radiance Fields with Cagescited by 78
  11. 2023UltraLiDAR: Learning Compact Representations for LiDAR Completion and Generationcited by 78
  12. 2025RayZer: A Self-supervised Large View Synthesis Modelcited by 77
  13. 2023Multiview Compressive Coding for 3D Reconstructioncited by 71
  14. 2022Towards Implicit Text-Guided 3D Shape Generationcited by 68
  15. 2021Unsupervised Learning of 3D Object Categories from Videos in the Wildcited by 67
  16. 2023AdaPoinTr: Diverse Point Cloud Completion with Adaptive Geometry-Aware Transformerscited by 67
  17. 2021Shelf-Supervised Mesh Prediction in the Wildcited by 65
  18. 2023Deep Learning on Implicit Neural Representations of Shapescited by 63
  19. 2021DOVE: Learning Deformable 3D Objects by Watching Videoscited by 61
  20. 2021Neural Fields as Learnable Kernels for 3D Reconstructioncited by 58
  21. 2021Sketch2Model: View-Aware 3D Modeling from Single Free-Hand Sketchescited by 54
  22. 2022Unsupervised Point Cloud Representation Learning with Deep Neural Networks: A Surveycited by 54
  23. 2022Few-View Object Reconstruction with Unknown Categories and Camera Posescited by 53
  24. 2023NAVI: Category-Agnostic Image Collections with High-Quality 3D Shape and Pose Annotationscited by 53
  25. 2024AGG: Amortized Generative 3D Gaussians for Single Image to 3Dcited by 53
  26. 2021Self-Contrastive Learning with Hard Negative Sampling for Self-supervised Point Cloud Learningcited by 52
  27. 2021Learning Generative Models of Textured 3D Meshes from Real-World Imagescited by 44
  28. 2021Planar Surface Reconstruction from Sparse Viewscited by 40
  29. 2022Understanding 3D Object Articulation in Internet Videoscited by 36
  30. 2021Multiresolution Deep Implicit Functions for 3D Shape Representationcited by 35
  31. 2024MVD-Fusion: Single-view 3D via Depth-consistent Multi-view Generationcited by 35
  32. 2021Neural Surface Mapscited by 34
  33. 2022Neural Template: Topology-aware Reconstruction and Disentangled Generation of 3D Meshescited by 34
  34. 20213D Spatial Recognition without Spatially Labeled 3Dcited by 31
  35. 2022Shape, Pose, and Appearance from a Single Image via Bootstrapped Radiance Field Inversioncited by 30
  36. 2022BNV-Fusion: Dense 3D Reconstruction using Bi-level Neural Volume Fusioncited by 29
  37. 2022SPAGHETTI: Editing Implicit Shapes Through Part Aware Generationcited by 28
  38. 20223PSDF: Three-Pole Signed Distance Function for Learning Surfaces with Arbitrary Topologiescited by 27

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