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.

翻译：准确估测活牛体重对于畜牧管理、动物福利及生产效率至关重要。传统方法（如使用走过式称重系统进行人工称重，或采用体况评分进行近似测量）涉及对牲畜的人工操作，可能从牲畜健康和经济角度影响生产效率。为解决这些问题，本研究探索了一种经济高效的非接触式活牛体重计算方法，该方法基于三维重建技术。所提出的流程利用多视角RGB图像，通过基于SAM 3D的一致性引导融合，继而进行集成回归。我们的方法为每头牛生成单一三维点云，并在低数据条件下比较了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在三维生成方法中表现最优，而经典集成模型在实际农场场景中提供了最稳定的性能（R$^2$ = 0.69 $\pm$ 0.10，MAPE = 2.22 $\pm$ 0.56 \%），这使其具备农场现场应用的可行性。这些发现表明，在难以大规模生成三维数据的农场环境中，提升重建质量比增加模型复杂度更为关键，这对于实现可扩展部署具有重要意义。