Computer Integrated Manufacturing System ›› 2022, Vol. 28 ›› Issue (11): 3652-3663.DOI: 10.13196/j.cims.2022.11.027

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Surface voxelization algorithm of color model based on multi-level subdivision

WANG Li1,2,ZHANG Xiangyun1,ZHOU Zhengyou1   

  1. 1.State Key Laboratory for Manufacturing Systems Engineering,Xi'an Jiaotong University
    2.National Institute Corporation of Additive Manufacturing
  • Online:2022-11-30 Published:2022-12-09
  • Supported by:
    Project supported by the National Key Research and Development Program,China (No.2018YFB1106803),and the Key Research and Development Program of Shannxi Province,China(No.2021ZDLGY10-09).

基于多级细分的彩色模型表面体素化算法

王莉1,2,张祥云1,周正友1   

  1. 1.西安交通大学机械制造系统工程国家重点实验室
    2.西安增材制造国家研究院有限公司
  • 基金资助:
    国家重点研发计划资助项目(2018YFB1106803);陕西省重点研发计划资助项目(2021ZDLGY10-09)。

Abstract: To make full use of the voxel-level printing advantages of Additive Manufacturing and realize rapid prototyping of multi-material / multi-colour models,a high accuracy voxelisation algorithm for color models was proposed.Triangular patches were evenly subdivided to obtain enough dense point cloud,which ensured that the surface of voxel model with small voxel threshold could be completely closed.To eliminate the influence of the model enclosing box size on the accuracy of the voxel model,on the basis of the octree voxelized representation,a linear subdivision of the voxel based on the ratio of side length of the voxel was proposed to greatly improve the accuracy of the surface voxel model.The examples demonstrated that the proposed algorithm could achieve high-resolution voxelisation of 3D color models and meet the accuracy requirements of Polyjet.

Key words: additive manufacturing, surface voxelisation, triangle encryption, octree, linear subdivision

摘要: 为充分发挥增材制造技术的体素级成型优势,实现多材料/多色彩模型的快速成型,提出一种彩色模型高精度体素化算法。该算法均匀细分三角面片,生成足够密集的点云数据,确保较小体素阈值对应的体素模型的表面能够完全封闭。针对模型包围盒形状对体素模型精度的影响,在八叉树体素化表示的基础上,提出了基于边长比值的体素线性细分方法,极大地提高了体素模型的表面精度。通过编程实例证明了该算法能够实现三维彩色模型的高分辨率体素化,体素化模型满足聚合物喷射工艺的精度需求。

关键词: 增材制造, 表面体素化, 三角形加密, 八叉树, 线性细分

CLC Number: