A Multi-View Real-Time Approach for Rapid Point Cloud Acquisition and Reconstruction in Goats

Yi Sun, Qifeng Li, Weihong Ma (), Mingyu Li, Anne De La Torre, Simon X. Yang and Chunjiang Zhao ()
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Yi Sun: College of Information Engineering, Northwest A&F University, Yangling 712100, China
Qifeng Li: Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
Weihong Ma: Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
Mingyu Li: Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
Anne De La Torre: Université Clermont Auvergne, INRAE, VetAgro Sup, UMR Herbivores, F-63122 Saint-Genès-Champanelle, France
Simon X. Yang: Advanced Robotics and Intelligent Systems Laboratory, School of Engineering, University of Guelph, Guelph, ON N1G 2W1, Canada
Chunjiang Zhao: College of Information Engineering, Northwest A&F University, Yangling 712100, China

Agriculture, 2024, vol. 14, issue 10, 1-22

Abstract: The body size, shape, weight, and scoring of goats are crucial indicators for assessing their growth, health, and meat production. The application of computer vision technology to measure these parameters is becoming increasingly prevalent. However, in real farm environments, obstacles, such as fences, ground conditions, and dust, pose significant challenges for obtaining accurate goat point cloud data. These obstacles lead to difficulties in rapid data extraction and result in incomplete reconstructions, causing substantial measurement errors. To address these challenges, we developed a system for real-time, non-contact acquisition, extraction, and reconstruction of goat point clouds using three depth cameras. The system operates in a scenario where goats walk naturally through a designated channel, and bidirectional distributed triggering logic is employed to ensure real-time acquisition of the point cloud. We also designed a noise recognition and filtering method tailored to handle complex environmental interferences found on farms, enabling automatic extraction of the goat point cloud. Furthermore, a distributed point cloud completion algorithm was developed to reconstruct missing sections of the goat point cloud caused by unavoidable factors such as railings and dust. Measurements of body height, body slant length, and chest circumference were calculated separately with deviation of no more than 25 mm and an average error of 3.1%. The system processes each goat in an average time of 3–5 s. This method provides rapid and accurate extraction and complementary reconstruction of 3D point clouds of goats in motion on real farms, without human intervention. It offers a valuable technological solution for non-contact monitoring and evaluation of goat body size, weight, shape, and appearance.

Keywords: target extraction; region segmentation; 3D reconstruction; point cloud completion (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
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