这篇论文搞了个 MedPCFM，用流匹配做医学点云补全，比扩散模型快7倍，在好几个头骨和下颌骨数据集上效果最好。
提出 PCFM 模型，基于 PTv3 和流匹配方法进行医学点云补全。在 SkullFix、SkullBreak 和 Mandibular Defect 三个数据集上评估。相对扩散模型（PCDiff），PCFM 采样步数大幅减少，在生成性能上达到最优。在最佳操作点，使用 PTv3 骨干比 PVCNN 快 7 倍。
MedPCFM: Improving Medical Point Cloud Completion by Integrating Point Transformers and Flow Matching
Medical point cloud completion is important for anatomical reconstruction and downstream clinical workflows, yet generative modeling in this setting remains insufficiently studied. We investigate completion through continuous-time generative modeling and introduce PCFM, a PTv3-backed flow matching approach for medical point cloud completion. We evaluate on SkullFix and SkullBreak, and additionally on the more recent Mandibular Defect dataset. We build strong baselines by adapting PTv3 to a deterministic encoder-decoder completion model and by instantiating diffusion completion (PCDiff) with both PVCNN and PTv3 denoisers. PCFM with PTv3 is competitive with the deterministic PTv3 baseline and achieves state-of-the-art generative performance across datasets, while requiring substantially fewer sampling steps than diffusion. At the best operating points, PTv3 also yields clear throughput gains, providing up to a 7$\times$ speed-up for PCFM compared to a PVCNN backbone. Finally, we study empirical scaling trends by varying model size and point cardinality, showing consistent gains with higher point resolution and informative trade-offs across model scales.