Multi-view real-time acquisition and 3D reconstruction of point clouds for beef cattle

Abstract

Body size, weight, and body condition score parameters are key indicators for monitoring cattle growth and they can be utilized to predict beef cattle yield and evaluate economic traits. However, it is easy to lay intense stress on cattle while measuring livestock’s body size manually, also along with giving negative effects on their feeding and weight gain. To resolve this problem, we design a real-time point cloud collection system for beef cattle with five depth cameras on a gantry structure. We developed point cloud preprocessing, registration, and 3D reconstruction algorithms, and quantitatively estimated the influence of light intensity during point cloud collection. The algorithms perform point cloud filtering, registration, segmentation, down-sampling, 3D reconstruction of the global point cloud, and target recognition. The maximum uncertainty of the calculated body width and length is 20 mm, and the acquisition time is within 0.08 s. We established a real-time system for 3D cattle point cloud- collection, which involves no stress on cattle when measuring. The point cloud collected by the system can provide technical support for the automatic extraction of key features during livestock body measurements.