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应用科学学报 >

2025 , Vol. 43 >Issue 2: 301 - 314

DOI: https://doi.org/10.3969/j.issn.0255-8297.2025.02.009

通信工程

基于多源点云特征信息的网格简化

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  • 1. 西安科技大学 测绘科学与技术学院, 陕西 西安 710054;
    2. 武汉大学 测绘遥感信息工程国家重点实验室, 湖北 武汉 430079

收稿日期: 2023-10-10

  网络出版日期: 2025-04-03

基金资助

国家自然科学基金（No.92038301）；自然资源部城市国土资源监测与仿真重点实验室开放基金（No.KF-2022-07-003）；长江水利委员会长江科学院开放研究基金（No.CKWV20231167/KF）；武汉大学-华为空间信息技术创新实验室项目（No.K22-4201-011）资助

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Mesh Simplification Based on Multi-source Point Cloud Feature Information

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  • 1. College of Geomatics, Xi'an University of Science and Technology, Xi'an 710054, Shaanxi, China;
    2. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, Hubei, China

Received date: 2023-10-10

  Online published: 2025-04-03

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摘要

针对基于二次误差函数的网格简化算法存在重要地理实体结构特征丢失的问题，提出一种基于多源点云特征信息的网格简化方法。首先，融合激光点云与影像密集匹配点云，以提高网格模型的质量。其次，结合点云颜色、高程及曲率等信息，基于超体素的区域生长算法对融合点云进行分割及特征信息的确定。最后，基于点云的特征信息对二次误差矩阵进行更新，从而实现基于融合点云的高精度网格简化。以融合点云构建的三维网格为实验数据，采用所提算法对其进行网格简化，并与QEM、QEF和Low-poly算法进行对比，结果表明：相较于其他三种算法，所提算法的简化精度平均提升39.49%。

关键词： 多源点云; 网格简化; 点云分割; 二次误差测度; 特征保持

本文引用格式

蒋萧, 邱春霞, 张春森, 葛英伟 . 基于多源点云特征信息的网格简化[J]. 应用科学学报, 2025 , 43(2) : 301 -314 . DOI: 10.3969/j.issn.0255-8297.2025.02.009

Abstract

To mitigate the significant loss of important geographic entity structural features in mesh simplification algorithms based on quadratic error functions, this paper presents a mesh simplification method that integrates multi-source point cloud feature information. Firstly, laser point clouds and image dense matching point clouds are fused to enhance the quality of the mesh model. Subsequently, incorporating attributes such as color, elevation and curvature, a region-growing algorithm based on super-voxels is applied to segment the fused point cloud and extract feature information. Finally, the quadratic error matrix is updated using the extracted point cloud feature information to achieve high-precision mesh simplification. Utilizing a three-dimensional mesh constructed from the fused point cloud as experimental data, the proposed algorithm is evaluated and compared with QEM, QEF, and Low-poly algorithms. Experimental results indicate that the proposed method improves simplification accuracy by an average of 39.49%.

Key words： multi-source point cloud; mesh simplification; point cloud segmentation; quadric error metric; feature preserving

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