用多视角图像和一致性融合技术，非接触估算活牛体重
Agreement-Driven Multi-View 3D Reconstruction for Live Cattle Weight Estimation
- 通过多视角图像与SAM 3D一致性引导融合生成3D点云
- 经典集成模型在低数据下表现稳定（R²=0.69±0.10，MAPE=2.22±0.56%）
- 适合农场实际部署，重建质量比模型复杂度更重要
准确估算活牛体重对畜牧管理、动物福利和生产效率至关重要。传统方法如过磅称重或体况评分需人工干预，影响生产效率和经济成本。为此，本研究提出一种低成本、非接触式的活牛体重估算方法，基于多视角RGB图像与SAM 3D一致性引导融合的3D重建，再结合集成回归。该方法为每头牛生成单一3D点云，并在低数据条件下对比经典集成模型与深度学习模型。结果表明，采用多视角一致性融合的SAM 3D优于其他3D生成方法；经典集成模型在实际农场场景中表现最稳定（R² = 0.69 ± 0.10，MAPE = 2.22 ± 0.56%），具备现场应用潜力。研究显示，在难以获取大量3D数据的农场环境中，提升重建质量比增加模型复杂度更关键。
原文摘要 · Abstract (English)
Accurate cattle live weight estimation is vital for livestock management, welfare, and productivity. Traditional methods, such as manual weighing using a walk-over weighing system or proximate measurements using body condition scoring, involve manual handling of stock and can impact productivity from both a stock and economic perspective. To address these issues, this study investigated a cost-effective, non-contact method for live weight calculation in cattle using 3D reconstruction. The proposed pipeline utilized multi-view RGB images with SAM 3D-based agreement-guided fusion, followed by ensemble regression. Our approach generates a single 3D point cloud per animal and compares classical ensemble models with deep learning models under low-data conditions. Results show that SAM 3D with multi-view agreement fusion outperforms other 3D generation methods, while classical ensemble models provide the most consistent performance for practical farm scenarios (R$^2$ = 0.69 $\pm$ 0.10, MAPE = 2.22 $\pm$ 0.56 \%), making this practical for on-farm implementation. These findings demonstrate that improving reconstruction quality is more critical than increasing model complexity for scalable deployment on farms where producing a large volume of 3D data is challenging.
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