PAL-Net: A Point-Wise CNN with Patch-Attention for 3D Facial Landmark Localization

• URL: http://arxiv.org/abs/2510.00910v1
• Date: Wed, 01 Oct 2025 13:52:35 GMT
• Title: PAL-Net: A Point-Wise CNN with Patch-Attention for 3D Facial Landmark Localization
• Authors: Ali Shadman Yazdi, Annalisa Cappella, Benedetta Baldini, Riccardo Solazzo, Gianluca Tartaglia, Chiarella Sforza, Giuseppe Baselli,
• Abstract summary: Manual annotation of anatomical landmarks on 3D facial scans is a time-consuming and expertise-dependent task.<n>This study presents a fully automated deep learning pipeline (PALNet) for localizing 50 anatomical landmarks on stereophotogrammetry facial models.
• Score: 0.4637385034504733
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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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