---
格式版本: 2
标题: "3D Weighted Geometric Graph Neural Networks for Sheep Facial Pain Assessment"
原文链接: "https://arxiv.org/abs/2608.11050"
发布日期: "2026-08-11"
发布时间校准状态: "found"
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发布时间校准时间: "2026-08-12T11:19:32+08:00"
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发现时间: "2026-08-12T11:16:29+08:00"
入库时间: "2026-08-12T03:19:32.682Z"
来源平台: "arXiv 学术论文搜索"
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AI优质: "否"
AI打分: 5
AI分档: "非优质"
AI质检状态: "不通过"
AI打分理由: "该资料为arXiv学术论文，研究羊面部疼痛评估的3D图神经网络，与超节点/AI Rack/机柜级AI基础设施完全无关，无任何技术或商业信号。"
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采集批次: "2026年8月12日2点04分20秒"
采集批次ID: "20260812-020420-289"
去重键: "https://arxiv.org/abs/2608.11050"
---

## Computer Science > Computer Vision and Pattern Recognition

## Title:3D Weighted Geometric Graph Neural Networks for Sheep Facial Pain Assessment

Authors:[Alam Noor](https://arxiv.org/search/cs?searchtype=author&query=Noor,+A), [Luis Almeida](https://arxiv.org/search/cs?searchtype=author&query=Almeida,+L), [Mohamed Daoudi](https://arxiv.org/search/cs?searchtype=author&query=Daoudi,+M)

[View PDF](https://arxiv.org/pdf/2608.11050) [HTML (experimental)](https://arxiv.org/html/2608.11050v1)

> Abstract: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.

| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) |
| --- | --- |
| Cite as: | [arXiv:2608.11050](https://arxiv.org/abs/2608.11050) \[cs.CV\] |
|  | (or [arXiv:2608.11050v1](https://arxiv.org/abs/2608.11050v1) \[cs.CV\] for this version) |
|  | [https://doi.org/10.48550/arXiv.2608.11050](https://doi.org/10.48550/arXiv.2608.11050) |

## Submission history

From: Alam Noor \[[view email](https://arxiv.org/show-email/07110e63/2608.11050)\]  
**\[v1\]** Tue, 11 Aug 2026 15:21:36 UTC (8,542 KB)

[Which authors of this paper are endorsers?](https://arxiv.org/auth/show-endorsers/2608.11050) | Disable MathJax ([What is MathJax?](https://info.arxiv.org/help/mathjax.html))
