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.

Accurate traffic prediction is crucial for real-time traffic management and a variety of subsequent applications. In the domain of traffic prediction, while the latest data-driven studies have achieved satisfactory results, they often overlook the significant problem of data sparsity (i.e., only part of the traffic system is observed) in real-world scenarios which can lead to insufficient data and erroneous predictions. Conventional strategies to counteract sparse data in traffic prediction problems typically involve a two-step process: initially imputing missing data, then predicting traffic state. A critical flaw in these existing methods is their assumption that traffic on adjacent roads equally influences unobserved roads, disregarding the impact of traffic management actions. In response to this challenge, we proposeTMA-GNN, an approach that integrates traffic management actions into traditional data-driven models, treating traffic signal control as governing rules. This approach constrains the attention mechanism between roads, enabling real-time updates of road connectivity and effectively capturing diverse vehicle behavior patterns such as emerging from a residential area or entering a parking lot. These enhancements not only address the limitations of ignoring traffic strategies but also challenge the conventional assumption that vehicles always travel strictly downstream from upstream segments, thereby improving traffic state estimations on both observed and unobserved roads. Nonetheless, in certain extreme scenarios (e.g., when a major residential area is connected by a single road), the unobserved road may be unpredictable and lead to inaccurate imputations. To address this,TMA-GNNintroduces a complementary strategy named EERA for evaluating the robustness of imputed values. This method effectively identifies roads with the highest uncertainty and prioritizes them for sensor installation, bridging data gaps on such unobserved roads. We conducted experiments on both synthetic and real-world datasets to validate the efficacy of theTMA-GNNframework and its components. Additionally, we carried out a series of case studies demonstrating the practical utility of EERA in real-world scenarios.

Traffic prediction is a crucial topic because of its broad scope of applications in the transportation domain. Though recent studies have achieved promising results, most of them cannot adequately deal with positions with no historical data, which is common due to limited resources in real life. Apart from this, the lack of uncertainty measurements also makes current models unable to manage risks, especially for the downstream tasks involving decision-making. Inspired by the previous inductive graph neural network, we proposed an uncertainty-aware framework to 1) extend prediction to locations with no historical records and significantly extend spatial coverage of prediction while reducing sensor deployment and 2) generate probabilistic prediction with uncertainty quantification to help the risk management. The experiment results show that our method achieved promising results on prediction tasks, and the uncertainty quantification gives consistent results that highly correlate with the locations with and without historical data. We also show that our model could help support sensor deployment tasks in the transportation field to achieve higher accuracy with a limited sensor deployment budget.