mlsanityData to 14 Oct 2026

A Point Set Generation Network for 3D Object Reconstruction from a Single Image

arXiv 1612.00603 ↗First posted 2 Dec 2016cs.CVcited by 979

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Cited by 65 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Generation of 3D data by deep neural network has been attracting increasing attention in the research community. The majority of extant works resort to regular representations such as volumetric grids or collection of images; however, these representations obscure the natural invariance of 3D shapes under geometric transformations and also suffer from a number of other issues. In this paper we address the problem of 3D reconstruction from a single image, generating a straight-forward form of output -- point cloud coordinates. Along with this problem arises a unique and interesting issue, that the groundtruth shape for an input image may be ambiguous. Driven by this unorthodox output form and the inherent ambiguity in groundtruth, we design architecture, loss function and learning paradigm that are novel and effective. Our final solution is a conditional shape sampler, capable of predicting multiple plausible 3D point clouds from an input image. In experiments not only can our system outperform state-of-the-art methods on single image based 3d reconstruction benchmarks; but it also shows a strong performance for 3d shape completion and promising ability in making multiple plausible predictions.

Key facts. Cited by 979 papers in the map (data through 14 Oct 2026). First posted on arXiv 2 Dec 2016.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 173; 2022: 200; 2023: 185; 2024: 167; 2025: 137; 2026: 117.

979 papers.

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  1. 2021NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstructioncited by 1,712
  2. 2023Zero-1-to-3: Zero-shot One Image to 3D Objectcited by 1,489
  3. 2023LRM: Large Reconstruction Model for Single Image to 3Dcited by 849
  4. 2022Point-E: A System for Generating 3D Point Clouds from Complex Promptscited by 699
  5. 2021Common Objects in 3D: Large-Scale Learning and Evaluation of Real-life 3D Category Reconstructioncited by 652
  6. 2023One-2-3-45: Any Single Image to 3D Mesh in 45 Seconds without Per-Shape Optimizationcited by 560
  7. 20213D Shape Generation and Completion through Point-Voxel Diffusioncited by 516
  8. 2022Masked Autoencoders for Point Cloud Self-supervised Learningcited by 459
  9. 2024CLAY: A Controllable Large-scale Generative Model for Creating High-quality 3D Assetscited by 455
  10. 2021ABO: Dataset and Benchmarks for Real-World 3D Object Understandingcited by 366
  11. 2023Splatter Image: Ultra-Fast Single-View 3D Reconstructioncited by 364
  12. 2021MonoScene: Monocular 3D Semantic Scene Completioncited by 363
38 more
  1. 2023VoxFormer: Sparse Voxel Transformer for Camera-based 3D Semantic Scene Completioncited by 313
  2. 2021PoinTr: Diverse Point Cloud Completion with Geometry-Aware Transformerscited by 286
  3. 2024PGSR: Planar-based Gaussian Splatting for Efficient and High-Fidelity Surface Reconstructioncited by 285
  4. 2023One-2-3-45++: Fast Single Image to 3D Objects with Consistent Multi-View Generation and 3D Diffusioncited by 283
  5. 2022Point-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-trainingcited by 264
  6. 2023Triplane Meets Gaussian Splatting: Fast and Generalizable Single-View 3D Reconstruction with Transformerscited by 258
  7. 2025SAM 3D: 3Dfy Anything in Imagescited by 247
  8. 2023DreamBooth3D: Subject-Driven Text-to-3D Generationcited by 235
  9. 2021Self-Supervised Pretraining of 3D Features on any Point-Cloudcited by 217
  10. 2023EmerNeRF: Emergent Spatial-Temporal Scene Decomposition via Self-Supervisioncited by 217
  11. 2022AutoSDF: Shape Priors for 3D Completion, Reconstruction and Generationcited by 208
  12. 2021Shape As Points: A Differentiable Poisson Solvercited by 187
  13. 2021Where2Act: From Pixels to Actions for Articulated 3D Objectscited by 182
  14. 2023NerfDiff: Single-image View Synthesis with NeRF-guided Distillation from 3D-aware Diffusioncited by 180
  15. 2024PointMamba: A Simple State Space Model for Point Cloud Analysiscited by 167
  16. 2021A System for General In-Hand Object Re-Orientationcited by 165
  17. 2022SinNeRF: Training Neural Radiance Fields on Complex Scenes from a Single Imagecited by 165
  18. 2023Contrast with Reconstruct: Contrastive 3D Representation Learning Guided by Generative Pretrainingcited by 164
  19. 2021GNeRF: GAN-based Neural Radiance Field without Posed Cameracited by 153
  20. 2022NeuralLift-360: Lifting An In-the-wild 2D Photo to A 3D Object with 360deg Viewscited by 148
  21. 2022Learning 3D Representations from 2D Pre-trained Models via Image-to-Point Masked Autoencoderscited by 144
  22. 2021BACON: Band-limited Coordinate Networks for Multiscale Scene Representationcited by 143
  23. 2022D2NeRF: Self-Supervised Decoupling of Dynamic and Static Objects from a Monocular Videocited by 137
  24. 2024ShapeLLM: Universal 3D Object Understanding for Embodied Interactioncited by 137
  25. 2022Diffusion-SDF: Conditional Generative Modeling of Signed Distance Functionscited by 133
  26. 2022Neural Wavelet-domain Diffusion for 3D Shape Generationcited by 132
  27. 2023Locally Attentional SDF Diffusion for Controllable 3D Shape Generationcited by 130
  28. 2023PointGPT: Auto-regressively Generative Pre-training from Point Cloudscited by 115
  29. 2021A-SDF: Learning Disentangled Signed Distance Functions for Articulated Shape Representationcited by 114
  30. 2021Density-aware Chamfer Distance as a Comprehensive Metric for Point Cloud Completioncited by 112
  31. 2023NeRF-DS: Neural Radiance Fields for Dynamic Specular Objectscited by 111
  32. 2023DiT-3D: Exploring Plain Diffusion Transformers for 3D Shape Generationcited by 110
  33. 2021Animatable Neural Radiance Fields from Monocular RGB Videoscited by 109
  34. 2022ShapeFormer: Transformer-based Shape Completion via Sparse Representationcited by 106
  35. 2023Neural Kernel Surface Reconstructioncited by 104
  36. 2021Neural Parts: Learning Expressive 3D Shape Abstractions with Invertible Neural Networkscited by 103
  37. 2021Oriented RepPoints for Aerial Object Detectioncited by 102
  38. 2022BigDatasetGAN: Synthesizing ImageNet with Pixel-wise Annotationscited by 99

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