Abstract:

[Significance] Livestock body-size parameters are important phenotypic indicators for evaluating growth and development, body condition, production performance, genetic evaluation, and breeding selection. Traditional manual measurement is time-consuming, labor-intensive, operator-dependent, and may induce stress and safety risks during animal restraint. Although two-dimensional imaging enables non-contact measurement, its lack of depth information limits the accurate estimation of girth-related traits and local body morphology. By directly describing three-dimensional body-surface structures, point clouds provide a promising basis for automated and objective livestock phenotyping. The aim of this review is to systematically summarize advances in three-dimensional point cloud-based livestock body measurement, to clarify the main technical workflow and methodological routes, to compare their characteristics and application scenarios, and to identify key challenges and research priorities for intelligent three-dimensional phenotyping and smart livestock farming. [Progress] Three-dimensional point cloud technology can reconstruct livestock body structures while retaining the advantages of non-contact measurement. It can be used for measuring linear traits, such as body length, body height, and body width; girth-related traits, including chest girth, abdominal girth, rump girth, and cannon circumference; and local morphological features such as rump shape, hindquarter fullness, and limb and hoof structure. With pigs, cattle, and sheep as the main research objects, this review examines the technical workflow of point cloud acquisition, preprocessing, completion, segmentation, keypoint localization, and body-size calculation. Livestock point cloud acquisition sensors and fixed, handheld, and mobile acquisition schemes are summarized. Preprocessing methods, including multi-view registration, filtering and denoising, posture normalization, and point cloud completion, are reviewed. These procedures can reduce background interference, motion distortion, occlusion, data loss, and posture variation, thereby improving measurement consistency. Two technical routes for body-size measurement are discussed. In the first route, anatomical keypoints are directly localized from complete point clouds using geometric features, projection analysis, curvature, skeleton models, or deep learning-based detectors. This route is suitable for linear measurements under standardized postures. In the second route, the animal is first segmented into anatomically relevant regions, after which keypoints or measurement sections are identified within the corresponding regions. By narrowing the search space, segmentation-based methods can reduce interference from unrelated body parts and are particularly suitable for variable postures, partial occlusion, and girth-related measurements. After keypoint localization or section determination, linear traits are generally calculated using Euclidean distances, whereas girth traits are estimated through contour extraction, curve fitting, and accumulated curve length. Overall, a development trend from manual annotation and rule-based geometric analysis toward automated methods integrating keypoint detection, point cloud segmentation, regional constraints, posture adaptation, and cross-species transfer is identified. [Conclusions and Prospects] Existing studies indicate that three-dimensional point cloud-based livestock body measurement has promising application prospects for automated, non-contact, and high-precision phenotype acquisition. However, several challenges remain, including unstable point cloud quality in complex farming environments; insufficient robustness, transferability, and edge-deployment capability of algorithms; limited multi-species and multi-posture datasets; and inconsistent definitions, annotations, and evaluation metrics. Future research should focus on improving acquisition accuracy, synchronization, and environmental adaptability and developing low-cost, lightweight, and reliable systems. Multimodal fusion of depth cameras, LiDAR, and RGB images, together with mobile platforms and multi-view acquisition, should be investigated for continuous measurement of group-housed animals. Robust, lightweight, and transferable models should be developed through self-supervised or unsupervised pretraining, semi-supervised learning, transfer learning, model pruning, knowledge distillation, and lightweight feature extraction. Open datasets should cover multiple species, breeds, growth stages, postures, and farming scenarios, accompanied by unified keypoint definitions, anatomical annotations, data partitions, and error metrics. In addition, body-size measurements should be integrated with body condition scoring, local morphological evaluation, growth and development monitoring, production performance analysis, genetic evaluation, and breeding selection. This will promote the transition from geometric parameter extraction to intelligent three-dimensional phenotypic analysis, ultimately achieving accurate, real-time, and high-throughput automatic livestock body measurement for precision feeding and smart livestock farming.

Key words: 3D point cloud, point cloud acquisition, data processing, deep learning, livestock body measurement