这篇论文用3D图神经网络给绵羊做疼痛评估，不用深度相机，单摄像头就能算，比传统2D方法更准，做动物福利或AI应用可以看看。
本文提出3D-SPFES系统，利用单目RGB相机和VideoDepthAnything估计3D空间，将SPFES面部标志（如耳朵、眼睛、鼻子）嵌入3D欧几里得空间。系统采用加权几何图神经网络（WG-GNN），包含K=3层几何感知消息传递和缩放点积注意力，增强解剖相关标志间信息。节点嵌入聚为O=3个疼痛级别簇，并整合为范围[0,100%]的归一化疼痛评分（NPS）。该方法避免专用深度硬件，提升疼痛评估的3D解剖准确性。
3D Weighted Geometric Graph Neural Networks for Sheep Facial Pain Assessment
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.