mlsanityData to 14 Oct 2026

A Survey of Medical Point Cloud Shape Learning: Registration, Reconstruction and Variation

arXiv 2508.03057 ↗First posted 5 Aug 2025eess.IV cs.CVcited by 2

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Abstract (arXiv)

Point clouds have become an increasingly important representation for 3D medical imaging, offering a compact, surface-preserving alternative to traditional voxel or mesh-based approaches. Recent advances in deep learning have enabled rapid progress in extracting, modeling, and analyzing anatomical shapes directly from point cloud data. This paper provides a comprehensive and systematic survey of learning-based shape analysis for medical point clouds, focusing on three fundamental tasks: registration, reconstruction, and variation modeling. We review recent literature from 2021 to 2025, summarize representative methods, datasets, and evaluation metrics, and highlight clinical applications and unique challenges in the medical domain. Key trends include the integration of hybrid representations, large-scale self-supervised models, and generative techniques. We also discuss current limitations, such as data scarcity, inter-patient variability, and the need for interpretable and robust solutions for clinical deployment. Finally, future directions are outlined for advancing point cloud-based shape learning in medical imaging.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 35 of its 70 references that are in the map. First posted on arXiv 5 Aug 2025.

Builds on

35 of 70 references.

References · 35

  1. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,248
  2. 2015ShapeNet: An Information-Rich 3D Model Repositorycited by 2,897
  3. 2018Dynamic Graph CNN for Learning on Point Cloudscited by 2,223
  4. 20163D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotationcited by 1,476
  5. 2020Point Transformercited by 1,470
  6. 2019Deep Learning for 3D Point Clouds: A Surveycited by 558
  7. 2019Med3D: Transfer Learning for 3D Medical Image Analysiscited by 288
  8. 2022FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settingscited by 125
  9. 2023SAM-Med3D: Towards General-purpose Segmentation Models for Volumetric Medical Imagescited by 107
  10. 20203D Deep Learning on Medical Images: A Reviewcited by 76
  11. 2020IntrA: 3D Intracranial Aneurysm Dataset for Deep Learningcited by 41
  12. 2023MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Visioncited by 32
23 more
  1. 2021DeepSSM: A Blueprint for Image-to-Shape Deep Learning Modelscited by 25
  2. 20213D Medical Point Transformer: Introducing Convolution to Attention Networks for Medical Point Cloud Analysiscited by 23
  3. 2021RibSeg Dataset and Strong Point Cloud Baselines for Rib Segmentation from CT Scanscited by 21
  4. 2023Mesh2SSM: From Surface Meshes to Statistical Shape Models of Anatomycited by 13
  5. 2021AxonEM Dataset: 3D Axon Instance Segmentation of Brain Cortical Regionscited by 11
  6. 2021Deep learning based geometric registration for medical images: How accurate can we get without visual features?cited by 10
  7. 2023Shape of my heart: Cardiac models through learned signed distance functionscited by 10
  8. 2025From Monocular Vision to Autonomous Action: Guiding Tumor Resection via 3D Reconstructioncited by 10
  9. 2023Can point cloud networks learn statistical shape models of anatomies?cited by 9
  10. 2021A Point Cloud Generative Model via Tree-Structured Graph Convolutions for 3D Brain Shape Reconstructioncited by 8
  11. 2023Modeling 3D cardiac contraction and relaxation with point cloud deformation networkscited by 7
  12. 2022PointNeuron: 3D Neuron Reconstruction via Geometry and Topology Learning of Point Cloudscited by 6
  13. 2023Efficient Anatomical Labeling of Pulmonary Tree Structures via Deep Point-Graph Representation-based Implicit Fieldscited by 6
  14. 2020Discriminative and Generative Models for Anatomical Shape Analysison Point Clouds with Deep Neural Networkscited by 5
  15. 2024Deep Learning-Based Point Cloud Registration: A Comprehensive Survey and Taxonomycited by 5
  16. 2024Point2SSM++: Self-Supervised Learning of Anatomical Shape Models from Point Cloudscited by 5
  17. 2024Weakly Supervised Bayesian Shape Modeling from Unsegmented Medical Imagescited by 4
  18. 2024LUDO: Low-Latency Understanding of Deformable Objects using Point Cloud Occupancy Functionscited by 4
  19. 2023Multi-objective point cloud autoencoders for explainable myocardial infarction predictioncited by 3
  20. 2024Coupled Laplacian Eigenmaps for Locally-Aware 3D Rigid Point Cloud Matchingcited by 3
  21. 2025Hierarchical Feature Learning for Medical Point Clouds via State Space Modelcited by 2
  22. 2022A multi-organ point cloud registration algorithm for abdominal CT registrationcited by 1
  23. 2024PointVoxelFormer -- Reviving point cloud networks for 3D medical imagingcited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Uncertainty Quantification for Cardiac Shape Reconstruction with Deep Signed Distance Functions via MCMC methodscited by 0
  2. 2026AnchorSplat: Fast and Structure Consistent Detail Synthesis for Gaussian Splattingcited by 0