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

TL;DR

This survey reviews recent advances in deep learning methods for medical point cloud shape analysis, focusing on registration, reconstruction, and variation modeling, highlighting trends, challenges, and future directions in clinical applications.

Contribution

It systematically summarizes recent literature (2021-2025) on learning-based medical point cloud analysis, emphasizing key methods, datasets, evaluation metrics, and clinical challenges.

Findings

Integration of hybrid representations and large-scale self-supervised models.

Use of generative techniques for shape variation modeling.

Identification of data scarcity and variability as key challenges.

Abstract

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.