Reconstruction and Body Size Detection of 3D Sheep Body Model Based on Point Cloud Data

Abstract : Aiming at the high workload, low precision, strong stress of the traditional manual measurement to obtain the sheep growth parameters, a novel measurement technology was proposed. The specimen of the Sunite sheep about 2–3 years old were chosen for study. By reverse engineering technology, point cloud data of sheep was captured by the 3D laser scanner. Because of noise point cloud data, the improved algorithm of k-nearest neighbor was used to process the data. To improve the subsequent processing time and efficiency, octree coding was employed to reduce data, which can get evenly distribution of point cloud data and retain sheep features. Then, 3D surface model of sheep body was reconstructed using Delaunay triangulation. Some parameters were extracted, including sheep body length, body height, hip height, hip width and chest width. Compared actual parameters values with computing values of two ways, by Geomagic platform and the proposed algorithms on the Matlab, average relative errors of two ways were 1.23% and 1.01%, respectively. So results of the proposed algorithm were with small error range. Using the point clouds can reconstruct sheep surface for computing body size without stress.
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Yanqing Zhou, Heru Xue, Chunlan Wang, Xinhua Jiang, Xiaojing Gao, et al.. Reconstruction and Body Size Detection of 3D Sheep Body Model Based on Point Cloud Data. 11th International Conference on Computer and Computing Technologies in Agriculture (CCTA), Aug 2017, Jilin, China. pp.251-262, ⟨10.1007/978-3-030-06179-1_26⟩. ⟨hal-02111533⟩

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