发表机构
CISTER Research Center; Faculty of Engineering University of Porto; Univ. Lille; CNRS; Centrale Lille; IMT Nord Europe; Institut Mines-Télécom; Centre for Digital Systems（CISTER研究中心; 波尔图大学工程学院; 里尔大学; 法国国家科学研究中心; 里尔中央理工学院; 北欧电信学院; 法国国立矿业与电信学院; 数字系统中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对现有深度学习忽略绵羊3D解剖与跨地标空间关系的问题，提出3D-SPFES系统，通过WG-GNN结合VideoDepthAnything实现绵羊面部疼痛评估，生成归一化疼痛评分。
AI 中文摘要
深度学习系统主要在2D单图像域内运行，将面部视为单维表示，忽略了绵羊的3D解剖结构以及经临床验证的绵羊疼痛面部表情量表（SPFES）所固有的跨地标空间关系。本文提出了一种新型单目深度感知几何图神经网络系统——3D绵羊疼痛面部表情系统（3D-SPFES），该系统通过VideoDepthAnything从单个RGB相机估计的3D欧氏空间中整合每个SPFES面部地标（如耳朵、眼睛和鼻子），从而无需专用深度硬件。每个地标节点包含特征向量，其中包含其3D空间坐标、估计的表面法线和面部属性类嵌入。连接节点的边根据聚合指标分配权重，该指标结合了3D空间中的欧氏距离和表面共面性。加权几何图神经网络（WG-GNN）使用$\boldsymbol{\textit{K}} = 3$个几何感知消息传递层研究此图，这些层通过缩放点积注意力方法增强，该方法可选择性增强与解剖结构相关的地标间消息。生成的节点嵌入被组合成$\boldsymbol{\textit{O}} = 3$个疼痛级别聚类，并集成到归一化疼痛评分（NPS）中，该评分在$[0, 100%]$范围内，是一种基于SPFES的置信度加权评分方法。
英文摘要
Deep learning systems perform mainly within the 2D for a single image domain and take the face as a single-dimension representation, losing sight of the 3D anatomy of sheep and cross-landmark spatial relationships that are intrinsic to the clinically proven Sheep Pain Facial Expression Scale (SPFES). This paper presents the \textbf{3D Sheep Pain Facial Expression System (3D-SPFES)}, a novel, monocular depth-aware geometric graph neural network system that integrates each SPFES facial landmark, such as the ears, eyes, and nose, into 3D Euclidean space estimated from a single RGB camera by using VideoDepthAnything, thus preventing the need for specialized depth hardware. Each landmark node includes a feature vector containing its 3D spatial coordinates, estimated surface normal, and facial attribute class embedding. Edges linked to nodes are assigned weights based on an aggregate metric that combines both Euclidean distance and surface co-planarity in a 3D space. A Weighted Geometric Graph Neural Network (WG-GNN) studies this graph using $\mathcal{K} = 3$ geometry-aware message-passing layers enhanced by a scaled dot-product attention method that selectively enhances anatomically relevant inter-landmark messages. The resultant node embeddings are combined into $\mathcal{O} = 3$ pain-level clusters and integrated into a Normalized Pain Score (NPS) within the range of $[0, 100%]$ a confidence-weighted, SPFES-derived scoring method.
