MMLG ‐Point: Unsupervised Pretraining Approach for Cattle Point Cloud Segmentation and Measurement

ABSTRACT Manual measurement of cattle body size presents challenges, such as inducing stress responses in animals and inefficiencies. For large livestock like cattle, measurement based on full point clouds involves extensive computations and interference between different cloud sections. To address this, we propose MMLG‐Point, a novel deep learning model for cattle point cloud segmentation and body size measurement, which introduces a Multilevel Geometric Perception Encoder and a Transformer‐based decoder architecture. The encoder integrates Kernel Point Convolution (KPConv) and Separable Structure‐Aware Learning (SSAL) with residual multiscale fusion to capture local geometric structures of large livestock point clouds, while the decoder employs CrossNorm and SelfNorm (CNSN) modules to enhance generalization under limited labeled data. Furthermore, an unsupervised pretraining strategy based on masked point reconstruction is proposed, enabling the model to learn structural and semantic representations from unlabeled cattle point clouds. Experimental results demonstrate that MMLG‐Point achieves outstanding segmentation accuracy with minimal supervision, obtaining an overall accuracy (OA) of 94.3% and a mean Intersection over Union (mIoU) of 89.4% on the Simmental cattle dataset using only 12 labeled samples. The model also exhibits strong cross‐species generalization, achieving 92.3% OA and 86.7% mIoU on pig datasets. Based on segmentation results, an automatic cattle body measurement algorithm is developed, incorporating density analysis, curvature detection, and contour extraction to compute parameters such as withers height, hip height, body length, chest girth, and abdominal circumference, achieving a mean absolute percentage error (MAPE) below 6%. These results confirm that the proposed MMLG‐Point framework provides an effective and generalizable approach for high‐precision segmentation and measurement of large livestock point clouds.