A 3D point cloud dataset of Jining Qing Goats for segmentation analysis and body size measurement
Year: 2026
Type: data-paper
Abstract: The rapid advancement of intelligent livestock farming and precision breeding has underscored the importance of non-contact body measurement and weight estimation. Based on 3D reconstruction, these techniques represent a critical pathway for the digital transformation of animal husbandry. However, there are no publicly available 3D point cloud datasets specific to Jining Qing Goats, particularly systematic data covering key developmental stages. To bridge this gap, we present a comprehensive dataset comprising multi-view 3D point clouds and standardized morphometric records of Jining Qing Goats. The dataset spans multiple age groups and emphasizes critical phases such as juvenile, growing, mature, and reproductive stages, thereby capturing a holistic representation of the breed's life cycle. During data acquisition, two Microsoft Kinect DK depth cameras were positioned bilaterally to capture RGB and depth images simultaneously under relatively static conditions. Multi-view point clouds were registered using the Iterative Closest Point (ICP) algorithm, with the floor plane serving as a unified reference to align all scans within a global coordinate system. In parallel, manual measurements of six key morphometric traits, including body length, withers height, shoulder width, abdominal width, heart girth and hip width, were collected as validation references. The dataset consists of raw RGB images, depth maps, point cloud files, camera calibration parameters, and manually annotated measurement records, all of which are openly accessible. This resource supports a wide range of computer vision tasks such as livestock 3D reconstruction, automated morphometric measurement, and weight estimation, thereby facilitating digital transformation, intelligent management, and sustainable development in modern livestock farming.
Source: Data in Brief
Authors: Kai Zhang, Qin Ma, Yichen Liu, Xiaochen Shi
Institution: China Agricultural University
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: gold
Estimated APC paid: $1,220
Funder: China Agricultural University
OpenAlex ID: W7124882520