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3D Point Capsule Networks

arXiv 1812.10775 ↗First posted 27 Dec 2018cs.CV cs.LG cs.NEcited by 106

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Abstract (arXiv)

In this paper, we propose 3D point-capsule networks, an auto-encoder designed to process sparse 3D point clouds while preserving spatial arrangements of the input data. 3D capsule networks arise as a direct consequence of our novel unified 3D auto-encoder formulation. Their dynamic routing scheme and the peculiar 2D latent space deployed by our approach bring in improvements for several common point cloud-related tasks, such as object classification, object reconstruction and part segmentation as substantiated by our extensive evaluations. Moreover, it enables new applications such as part interpolation and replacement.

Key facts. Cited by 106 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 Dec 2018.

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Citing papers per year: 2021: 37; 2022: 39; 2023: 17; 2024: 10; 2025: 2; 2026: 1.

106 papers.

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  1. 2023A Survey on Self-supervised Learning: Algorithms, Applications, and Future Trendscited by 205
  2. 2022CrossPoint: Self-Supervised Cross-Modal Contrastive Learning for 3D Point Cloud Understandingcited by 198
  3. 2021Spatio-temporal Self-Supervised Representation Learning for 3D Point Cloudscited by 180
  4. 2023Neural Kernel Surface Reconstructioncited by 102
  5. 2021SCALE: Modeling Clothed Humans with a Surface Codec of Articulated Local Elementscited by 72
  6. 2024Position: Topological Deep Learning is the New Frontier for Relational Learningcited by 65
  7. 2021Neural Fields as Learnable Kernels for 3D Reconstructioncited by 58
  8. 2023FreeZe: Training-free zero-shot 6D pose estimation with geometric and vision foundation modelscited by 56
  9. 2022Unsupervised Point Cloud Representation Learning with Deep Neural Networks: A Surveycited by 54
  10. 2021Self-Contrastive Learning with Hard Negative Sampling for Self-supervised Point Cloud Learningcited by 52
  11. 2021Point-set Distances for Learning Representations of 3D Point Cloudscited by 48
  12. 2022Comprehensive Review of Deep Learning-Based 3D Point Cloud Completion Processing and Analysiscited by 44
38 more
  1. 2021Learning general and distinctive 3D local deep descriptors for point cloud registrationcited by 41
  2. 2021Orienting Point Clouds with Dipole Propagationcited by 31
  3. 2022Transformers in 3D Point Clouds: A Surveycited by 31
  4. 2021Deep Learning Based 3D Segmentation: A Surveycited by 30
  5. 2022Masked Autoencoders in 3D Point Cloud Representation Learningcited by 30
  6. 2024Point-JEPA: A Joint Embedding Predictive Architecture for Self-Supervised Learning on Point Cloudcited by 29
  7. 2021Bootstrap Your Own Correspondencescited by 27
  8. 2023SGFormer: Semantic Graph Transformer for Point Cloud-based 3D Scene Graph Generationcited by 27
  9. 2023Bridging the Domain Gap: Self-Supervised 3D Scene Understanding with Foundation Modelscited by 26
  10. 2021Multimodal Semi-Supervised Learning for 3D Objectscited by 24
  11. 2021Found a Reason for me? Weakly-supervised Grounded Visual Question Answering using Capsulescited by 22
  12. 2021PvDeConv: Point-Voxel Deconvolution for Autoencoding CAD Construction in 3Dcited by 20
  13. 2021Centroid Transformers: Learning to Abstract with Attentioncited by 20
  14. 2022Distillation with Contrast is All You Need for Self-Supervised Point Cloud Representation Learningcited by 19
  15. 2021A Fine-Grained Dataset and its Efficient Semantic Segmentation for Unstructured Driving Scenarioscited by 18
  16. 2022RIM-Net: Recursive Implicit Fields for Unsupervised Learning of Hierarchical Shape Structurescited by 18
  17. 20223D-OAE: Occlusion Auto-Encoders for Self-Supervised Learning on Point Cloudscited by 18
  18. 2021Label-Efficient Point Cloud Semantic Segmentation: An Active Learning Approachcited by 17
  19. 2022GRASP-Net: Geometric Residual Analysis and Synthesis for Point Cloud Compressioncited by 17
  20. 2021Unsupervised Motion Representation Learning with Capsule Autoencoderscited by 16
  21. 20213D Compositional Zero-shot Learning with DeCompositional Consensuscited by 16
  22. 2022POS-BERT: Point Cloud One-Stage BERT Pre-Trainingcited by 16
  23. 2024A Comprehensive Survey of Convolutions in Deep Learning: Applications, Challenges, and Future Trendscited by 16
  24. 2022Masked Surfel Prediction for Self-Supervised Point Cloud Learningcited by 15
  25. 2022AGConv: Adaptive Graph Convolution on 3D Point Cloudscited by 13
  26. 2023Iterative Superquadric Recomposition of 3D Objects from Multiple Viewscited by 13
  27. 2021SIENet: Spatial Information Enhancement Network for 3D Object Detection from Point Cloudcited by 12
  28. 2021Progressive Seed Generation Auto-encoder for Unsupervised Point Cloud Learningcited by 12
  29. 20213D Point Cloud Registration with Multi-Scale Architecture and Unsupervised Transfer Learningcited by 11
  30. 2022Data Efficient 3D Learner via Knowledge Transferred from 2D Modelcited by 10
  31. 2022PointMCD: Boosting Deep Point Cloud Encoders via Multi-view Cross-modal Distillation for 3D Shape Recognitioncited by 10
  32. 2022MvDeCor: Multi-view Dense Correspondence Learning for Fine-grained 3D Segmentationcited by 10
  33. 2021Exploiting Local Geometry for Feature and Graph Construction for Better 3D Point Cloud Processing with Graph Neural Networkscited by 9
  34. 2021Training or Architecture? How to Incorporate Invariance in Neural Networkscited by 9
  35. 2022CP-Net: Contour-Perturbed Reconstruction Network for Self-Supervised Point Cloud Learningcited by 9
  36. 2022Canonical Fields: Self-Supervised Learning of Pose-Canonicalized Neural Fieldscited by 9
  37. 2021Robust Partial-to-Partial Point Cloud Registration in a Full Rangecited by 8
  38. 2022VRNet: Learning the Rectified Virtual Corresponding Points for 3D Point Cloud Registrationcited by 8

50 most cited of 106 All 106 (JSON)