Monocular Mesh Recovery and Body Measurement of Female Saanen Goats

TL;DR

This work addresses the lack of high-quality 3D data and species-specific models for Saanen dairy goats in precision livestock farming. It introduces the FemaleSaanenGoat eight-view RGBD dataset and the SaanenGoat parametric model, enabling accurate 3D reconstruction and automated six-parameter body measurements from single views. The approach shows substantial improvements over generic SMAL/SMAL+ baselines in both mesh reconstruction (up to 77.7% error reduction on In-Shape) and body-mimension accuracy (MAE of 1.90, beating SMAL 4.89 and SMAL+ 3.48) and demonstrates robust monocular recovery capabilities. These results establish a practical, scalable framework for 3D phenotyping in precision farming, with ongoing work addressing occlusions and multi-breed generalization.

Abstract

The lactation performance of Saanen dairy goats, renowned for their high milk yield, is intrinsically linked to their body size, making accurate 3D body measurement essential for assessing milk production potential, yet existing reconstruction methods lack goat-specific authentic 3D data. To address this limitation, we establish the FemaleSaanenGoat dataset containing synchronized eight-view RGBD videos of 55 female Saanen goats (6-18 months). Using multi-view DynamicFusion, we fuse noisy, non-rigid point cloud sequences into high-fidelity 3D scans, overcoming challenges from irregular surfaces and rapid movement. Based on these scans, we develop SaanenGoat, a parametric 3D shape model specifically designed for female Saanen goats. This model features a refined template with 41 skeletal joints and enhanced udder representation, registered with our scan data. A comprehensive shape space constructed from 48 goats enables precise representation of diverse individual variations. With the help of SaanenGoat model, we get high-precision 3D reconstruction from single-view RGBD input, and achieve automated measurement of six critical body dimensions: body length, height, chest width, chest girth, hip width, and hip height. Experimental results demonstrate the superior accuracy of our method in both 3D reconstruction and body measurement, presenting a novel paradigm for large-scale 3D vision applications in precision livestock farming.

Figures (10)

• Figure 1: Pose variation examples in the Saanen dataset. (a) standing, (b) walking, (c) head turning, (d) looking backward, and (e) head lowering. Top row: fusion meshes after denoising; Middle row: manually labeled 3D surface keypoints; Bottom row: source depth images of one view.
• Figure 2: Data Processing Pipeline. We capture RGBD data of Saanen dairy goats using top-view and seven surrounding cameras. Multi-view dynamic fusion generates geometrically accurate 3D goat models. The Saanen Goat Initialization Template is registered to the scanned data, followed by pose normalization to T-pose for shape space construction.
• Figure 3: Examples of scan results for Saanen dairy goats of different ages and body types included in the dataset.
• Figure 4: Comparison between the average model of our SaanenGoat (top) and the SMAL (bottom). Learned from real goat scan meshes, the SaanenGoat model provides a more accurate fit to the real goat's anatomy than SMAL.
• Figure 5: Visualization of the shape space for Saanen goats. The central grid represents the average shape, while the surrounding variations demonstrate different shape changes in the model. We visualize the first four principal components with deviations of ±2 standard deviations.