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Multiresolution Tree Networks for 3D Point Cloud Processing

arXiv 1807.03520 ↗First posted 10 Jul 2018cs.CV cs.GR cs.LGcited by 91

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Abstract (arXiv)

We present multiresolution tree-structured networks to process point clouds for 3D shape understanding and generation tasks. Our network represents a 3D shape as a set of locality-preserving 1D ordered list of points at multiple resolutions. This allows efficient feed-forward processing through 1D convolutions, coarse-to-fine analysis through a multi-grid architecture, and it leads to faster convergence and small memory footprint during training. The proposed tree-structured encoders can be used to classify shapes and outperform existing point-based architectures on shape classification benchmarks, while tree-structured decoders can be used for generating point clouds directly and they outperform existing approaches for image-to-shape inference tasks learned using the ShapeNet dataset. Our model also allows unsupervised learning of point-cloud based shapes by using a variational autoencoder, leading to higher-quality generated shapes.

Key facts. Cited by 91 papers in the map (data through 7 Oct 2026). First posted on arXiv 10 Jul 2018.

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Citing papers per year: 2021: 31; 2022: 32; 2023: 16; 2024: 8; 2025: 4.

91 papers.

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  1. 2021Diffusion Probabilistic Models for 3D Point Cloud Generationcited by 734
  2. 20213D Shape Generation and Completion through Point-Voxel Diffusioncited by 516
  3. 2021Self-Supervised Pretraining of 3D Features on any Point-Cloudcited by 217
  4. 2023A Survey on Self-supervised Learning: Algorithms, Applications, and Future Trendscited by 205
  5. 2022CrossPoint: Self-Supervised Cross-Modal Contrastive Learning for 3D Point Cloud Understandingcited by 198
  6. 2021Spatio-temporal Self-Supervised Representation Learning for 3D Point Cloudscited by 180
  7. 20234DGen: Grounded 4D Content Generation with Spatial-temporal Consistencycited by 117
  8. 2023PointGPT: Auto-regressively Generative Pre-training from Point Cloudscited by 115
  9. 2023DiT-3D: Exploring Plain Diffusion Transformers for 3D Shape Generationcited by 110
  10. 2022Learning to Generate Realistic LiDAR Point Cloudscited by 91
  11. 2022Surface Representation for Point Cloudscited by 84
  12. 2024Generating CAD Code with Vision-Language Models for 3D Designscited by 61
38 more
  1. 2021Regularization Strategy for Point Cloud via Rigidly Mixed Samplecited by 59
  2. 2021PRA-Net: Point Relation-Aware Network for 3D Point Cloud Analysiscited by 58
  3. 2022Unsupervised Point Cloud Representation Learning with Deep Neural Networks: A Surveycited by 54
  4. 2021Self-Contrastive Learning with Hard Negative Sampling for Self-supervised Point Cloud Learningcited by 52
  5. 2023Joint-MAE: 2D-3D Joint Masked Autoencoders for 3D Point Cloud Pre-trainingcited by 52
  6. 2021Learning Generative Models of Textured 3D Meshes from Real-World Imagescited by 44
  7. 2021RandomRooms: Unsupervised Pre-training from Synthetic Shapes and Randomized Layouts for 3D Object Detectioncited by 37
  8. 2022Autoregressive 3D Shape Generation via Canonical Mappingcited by 37
  9. 2022Transformers in 3D Point Clouds: A Surveycited by 31
  10. 2021Learning Inner-Group Relations on Point Cloudscited by 29
  11. 2022Weakly Supervised Semantic Segmentation for Large-Scale Point Cloudcited by 27
  12. 2024Wasserstein Flow Matching: Generative modeling over families of distributionscited by 27
  13. 2022SurfGen: Adversarial 3D Shape Synthesis with Explicit Surface Discriminatorscited by 26
  14. 2023Bridging the Domain Gap: Self-Supervised 3D Scene Understanding with Foundation Modelscited by 26
  15. 2022FBNet: Feedback Network for Point Cloud Completioncited by 20
  16. 2022Distillation with Contrast is All You Need for Self-Supervised Point Cloud Representation Learningcited by 19
  17. 20223D-OAE: Occlusion Auto-Encoders for Self-Supervised Learning on Point Cloudscited by 18
  18. 2023Learning to Estimate 6DoF Pose from Limited Data: A Few-Shot, Generalizable Approach using RGB Imagescited by 18
  19. 2021Go with the Flows: Mixtures of Normalizing Flows for Point Cloud Generation and Reconstructioncited by 16
  20. 2021GLASS: Geometric Latent Augmentation for Shape Spacescited by 16
  21. 2022POS-BERT: Point Cloud One-Stage BERT Pre-Trainingcited by 16
  22. 2022CO^3: Cooperative Unsupervised 3D Representation Learning for Autonomous Drivingcited by 15
  23. 2022Masked Surfel Prediction for Self-Supervised Point Cloud Learningcited by 15
  24. 2022TetraDiffusion: Tetrahedral Diffusion Models for 3D Shape Generationcited by 14
  25. 2022AGConv: Adaptive Graph Convolution on 3D Point Cloudscited by 13
  26. 2021Progressive Seed Generation Auto-encoder for Unsupervised Point Cloud Learningcited by 12
  27. 2022MvDeCor: Multi-view Dense Correspondence Learning for Fine-grained 3D Segmentationcited by 10
  28. 2022CP-Net: Contour-Perturbed Reconstruction Network for Self-Supervised Point Cloud Learningcited by 9
  29. 2022Unsupervised Point Cloud Pre-Training via Contrasting and Clusteringcited by 9
  30. 2023FrePolad: Frequency-Rectified Point Latent Diffusion for Point Cloud Generationcited by 9
  31. 2021Language-Level Semantics Conditioned 3D Point Cloud Segmentationcited by 7
  32. 2021Hierarchical View Predictor: Unsupervised 3D Global Feature Learning through Hierarchical Prediction among Unordered Viewscited by 7
  33. 2022NeuralODF: Learning Omnidirectional Distance Fields for 3D Shape Representationcited by 7
  34. 2021Local and Global Point Cloud Reconstruction for 3D Hand Pose Estimationcited by 6
  35. 2021PriFit: Learning to Fit Primitives Improves Few Shot Point Cloud Segmentationcited by 6
  36. 2023BuilDiff: 3D Building Shape Generation using Single-Image Conditional Point Cloud Diffusion Modelscited by 6
  37. 2021Learning Mesh Representations via Binary Space Partitioning Tree Networkscited by 5
  38. 2021Point Discriminative Learning for Data-efficient 3D Point Cloud Analysiscited by 5

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