Agreement-Driven Multi-View 3D Reconstruction for Live Cattle Weight Estimation

2026-01-25

AI Summary

This study addresses the limitations of traditional cattle weight estimation methods, which rely on manual weighing or body condition scoring and are inefficient while compromising animal welfare. To overcome these challenges, the authors propose a non-contact, low-cost approach that leverages multi-view RGB images for 3D reconstruction. By integrating SAM 3D with a multi-view consistency-guided point cloud fusion strategy, the method generates high-quality individual point clouds, which are then used by an ensemble regression model to predict live weight. Emphasizing reconstruction fidelity over model complexity, the approach demonstrates robust performance even under limited data conditions. Evaluated in real-world farm settings, the system achieves an R² of 0.69 ± 0.10 and a mean absolute percentage error (MAPE) of 2.22% ± 0.56%, confirming its practicality and deployability for precision livestock farming.

Technology Category

• Computer Vision: 3D Computer Vision
• Intelligent Robots: State Estimation
• Machine Learning: Multi-instance/Multi-view Learning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

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.

Problem

• live cattle weight estimation
• 3D reconstruction
• non-contact measurement
• multi-view fusion
• livestock management

Innovation

• multi-view 3D reconstruction
• agreement-driven fusion
• SAM 3D
• ensemble regression
• live cattle weight estimation