您好，欢迎访问北京市农林科学院 机构知识库！
北京市农林科学院机构知识库

全部

  • 全部
  • 标题
  • 学者
  • 机构
  • 关键词

A reconstruction method for incomplete pig point clouds based on stepwise hole filling and its applications

文献类型： 外文期刊

作者： Xu, Zhankang 1 ; Li, Qifeng 2 ; Ma, Weihong 2 ; Li, Mingyu 2 ; Xue, Xianglong 2 ; Zhao, Chunjiang 1 ;

作者机构： 1.Northwest A&F Univ, Coll Informat Engn, Yangling 712100, Peoples R China

2.Beijing Acad Agr & Forestry Sci, Informat Technol Res Ctr, Beijing 100097, Peoples R China

3.Natl Innovat Ctr Digital Technol Anim Husb, Beijing 100097, Peoples R China

4.Natl Innovat Ctr Digital Seed Ind, Beijing 100097, Peoples R China

关键词： 3D reconstruction; 3D point cloud; Hole filling; Pig body size measurement; Pig weight estimation

期刊名称：BIOSYSTEMS ENGINEERING （ 影响因子：5.3； 五年影响因子：5.9 ）

ISSN： 1537-5110

年卷期： 2025 年 255 卷

页码：

收录情况： SCI

摘要： The 3D model accurately depicts the surface characteristics of pigs, enabling measurement of their body size and prediction of the weight. However, multi-view 3D point cloud reconstructions of pigs often suffer from significant missing areas in leg and torso regions due to factors like railing obstructions and camera blind spots. To address this issue, this paper proposes a method for reconstructing incomplete pig point clouds based on stepwise hole filling. This approach converts the point cloud into mesh, initially filling part of the large, high-curvature holes that are difficult to handle based on pig morphology to narrow their extent, followed by filling remaining areas. Experimental results show that the completion effect of this method is visually superior to existing completion methods. The mean relative errors for calculating cannon bone girth, chest girth, and abdominal girth using the completed model compared to manual measurements were 5.04 %, 3.83 %, and 3.51 %, respectively, representing reductions of 1.24 %, 11.47 %, and 9.48 % compared to the method of directly using incomplete point clouds. In addition, utilizing the watertight properties of the mesh model completed by this method, the volume of the pig was calculated, and a volume-based Logistic regression weight estimation model was established, achieving a mean absolute percentage error (MAPE) of 4.06 %. This underscores its high precision in estimating pig weight.

  • 相关文献
作者其他论文 更多>>
置顶
购物车
反馈

© 京ICP备09089781号-29 主办单位：北京市农林科学院

学术资源： 中国农业科学院 农业专业知识服务系统 农科联盟资源共建共享平台 中国农业科技文献与信息服务平台 中国农业期刊集成服务平台