用点注意力网络自动定位3D人脸50个解剖点，精度媲美人工标注。
PAL-Net: A Point-Wise CNN with Patch-Attention for 3D Facial Landmark Localization
- 采用局部注意力的点级卷积网络，结合区域筛选与粗对齐提升定位精度。
- 在214例健康成人数据上误差仅3.686mm，距离误差2.822mm，接近人评一致性。
- 轻量高效，跨数据集泛化性强，适合临床高通量分析场景。
3D人脸扫描的解剖标志点手动标注耗时且依赖经验，但对临床评估、形态测量和颅面研究至关重要。尽管已有多种深度学习方法用于面部标志点定位，多数聚焦伪标志点或需复杂输入表示，限制了其临床应用。本文提出一种全自动深度学习流程PAL-Net，用于立体摄影测量人脸模型上50个解剖标志点的定位。该方法结合粗对齐、感兴趣区域过滤及初始近似，采用基于块的点级卷积神经网络并引入注意力机制。在214例健康成人标注数据上训练与评估，平均定位误差为3.686 mm，关键解剖距离误差为2.822 mm，接近人评变异水平。为进一步评估泛化能力，在FaceScape数据集700名受试者上测试，点误差达0.41 mm，距离误差0.38 mm。相比现有方法，PAL-Net在精度与计算成本间取得良好平衡。虽在网格质量差区域（如耳部、发际线）性能下降，但在多数解剖区域保持稳定准确。该模型在不同数据集和面部区域均表现出色，点定位与结构评估均优于现有方法，提供了一种轻量、可扩展的3D人体测量分析方案，有望支持临床工作流并减少手动标注依赖。源代码见：https://github.com/Ali5hadman/PAL-Net-A-Point-Wise-CNN-with-Patch-Attention
原文摘要 · Abstract (English)
Manual annotation of anatomical landmarks on 3D facial scans is a time-consuming and expertise-dependent task, yet it remains critical for clinical assessments, morphometric analysis, and craniofacial research. While several deep learning methods have been proposed for facial landmark localization, most focus on pseudo-landmarks or require complex input representations, limiting their clinical applicability. This study presents a fully automated deep learning pipeline (PAL-Net) for localizing 50 anatomical landmarks on stereo-photogrammetry facial models. The method combines coarse alignment, region-of-interest filtering, and an initial approximation of landmarks with a patch-based pointwise CNN enhanced by attention mechanisms. Trained and evaluated on 214 annotated scans from healthy adults, PAL-Net achieved a mean localization error of 3.686 mm and preserves relevant anatomical distances with a 2.822 mm average error, comparable to intra-observer variability. To assess generalization, the model was further evaluated on 700 subjects from the FaceScape dataset, achieving a point-wise error of 0.41\,mm and a distance-wise error of 0.38\,mm. Compared to existing methods, PAL-Net offers a favorable trade-off between accuracy and computational cost. While performance degrades in regions with poor mesh quality (e.g., ears, hairline), the method demonstrates consistent accuracy across most anatomical regions. PAL-Net generalizes effectively across datasets and facial regions, outperforming existing methods in both point-wise and structural evaluations. It provides a lightweight, scalable solution for high-throughput 3D anthropometric analysis, with potential to support clinical workflows and reduce reliance on manual annotation. Source code can be found at https://github.com/Ali5hadman/PAL-Net-A-Point-Wise-CNN-with-Patch-Attention
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