mlsanityData to 7 Oct 2026

PointInverter: Point Cloud Reconstruction and Editing via a Generative Model with Shape Priors

arXiv 2211.08702 ↗First posted 16 Nov 2022cs.CV cs.AI cs.GRcited by 5

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Abstract (arXiv)

In this paper, we propose a new method for mapping a 3D point cloud to the latent space of a 3D generative adversarial network. Our generative model for 3D point clouds is based on SP-GAN, a state-of-the-art sphere-guided 3D point cloud generator. We derive an efficient way to encode an input 3D point cloud to the latent space of the SP-GAN. Our point cloud encoder can resolve the point ordering issue during inversion, and thus can determine the correspondences between points in the generated 3D point cloud and those in the canonical sphere used by the generator. We show that our method outperforms previous GAN inversion methods for 3D point clouds, achieving state-of-the-art results both quantitatively and qualitatively. Our code is available at https://github.com/hkust-vgd/point_inverter.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 39 of its 44 references that are in the map. First posted on arXiv 16 Nov 2022.

Builds on

39 of 44 references.

References · 39

  1. 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 5,986
  2. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,223
  3. 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,384
  4. 2019Analyzing and Improving the Image Quality of StyleGANcited by 3,580
  5. 2017ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenescited by 3,088
  6. 2015ShapeNet: An Information-Rich 3D Model Repositorycited by 2,880
  7. 2018Dynamic Graph CNN for Learning on Point Cloudscited by 2,220
  8. 2016A Point Set Generation Network for 3D Object Reconstruction from a Single Imagecited by 972
  9. 2018PointCNN: Convolution On X-Transformed Pointscited by 764
  10. 20173D Semantic Segmentation with Submanifold Sparse Convolutional Networkscited by 746
  11. 2020Encoding in Style: a StyleGAN Encoder for Image-to-Image Translationcited by 727
  12. 2019RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Cloudscited by 584
27 more
  1. 2019Deep Learning for 3D Point Clouds: A Surveycited by 557
  2. 2019Revisiting Point Cloud Classification: A New Benchmark Dataset and Classification Model on Real-World Datacited by 532
  3. 2019PointFlow: 3D Point Cloud Generation with Continuous Normalizing Flowscited by 487
  4. 2021Pivotal Tuning for Latent-based Editing of Real Imagescited by 427
  5. 2020In-Domain GAN Inversion for Real Image Editingcited by 420
  6. 2017Large-scale Point Cloud Semantic Segmentation with Superpoint Graphscited by 384
  7. 20163D Menagerie: Modeling the 3D shape and pose of animalscited by 310
  8. 2018SpiderCNN: Deep Learning on Point Sets with Parameterized Convolutional Filterscited by 261
  9. 2021ReStyle: A Residual-Based StyleGAN Encoder via Iterative Refinementcited by 248
  10. 2020Learning Gradient Fields for Shape Generationcited by 245
  11. 2021HyperStyle: StyleGAN Inversion with HyperNetworks for Real Image Editingcited by 208
  12. 20193D Point Cloud Generative Adversarial Network Based on Tree Structured Graph Convolutionscited by 173
  13. 2020SqueezeSegV3: Spatially-Adaptive Convolution for Efficient Point-Cloud Segmentationcited by 170
  14. 2021Deep Parametric Continuous Convolutional Neural Networkscited by 161
  15. 2018PointGrow: Autoregressively Learned Point Cloud Generation with Self-Attentioncited by 105
  16. 2019ShellNet: Efficient Point Cloud Convolutional Neural Networks using Concentric Shells Statisticscited by 104
  17. 2020SoftFlow: Probabilistic Framework for Normalizing Flow on Manifoldscited by 97
  18. 2020Discrete Point Flow Networks for Efficient Point Cloud Generationcited by 87
  19. 2021HyperInverter: Improving StyleGAN Inversion via Hypernetworkcited by 85
  20. 2018Recurrent Slice Networks for 3D Segmentation of Point Cloudscited by 82
  21. 2021Unsupervised 3D Shape Completion through GAN Inversioncited by 70
  22. 2019Unpaired Point Cloud Completion on Real Scans using Adversarial Trainingcited by 66
  23. 2020Progressive Point Cloud Deconvolution Generation Networkcited by 46
  24. 2021SP-GAN: Sphere-Guided 3D Shape Generation and Manipulationcited by 21
  25. 2019Spectral-GANs for High-Resolution 3D Point-cloud Generationcited by 17
  26. 2020MRGAN: Multi-Rooted 3D Shape Generation with Unsupervised Part Disentanglementcited by 15
  27. 2020A Progressive Conditional Generative Adversarial Network for Generating Dense and Colored 3D Point Cloudscited by 10

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Cited by year

Citing papers per year: 2023: 4; 2024: 1.

5 papers.

  1. 2023ShapeGPT: 3D Shape Generation with A Unified Multi-modal Language Modelcited by 38
  2. 2023FrePolad: Frequency-Rectified Point Latent Diffusion for Point Cloud Generationcited by 9
  3. 2023Advances in 3D Neural Stylization: A Surveycited by 8
  4. 2023IPVNet: Learning Implicit Point-Voxel Features for Open-Surface 3D Reconstructioncited by 0
  5. 2024Exploiting Topological Priors for Boosting Point Cloud Generationcited by 0