中文

Journal of Applied Sciences >

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

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

Communication Engineering

Mesh Simplification Based on Multi-source Point Cloud Feature Information

Expand
  • 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

Fold

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

Cite this article

JIANG Xiao, QIU Chunxia, ZHANG Chunsen, GE Yingwei . Mesh Simplification Based on Multi-source Point Cloud Feature Information[J]. Journal of Applied Sciences, 2025 , 43(2) : 301 -314 . DOI: 10.3969/j.issn.0255-8297.2025.02.009

References

[1] 王维, 王晨阳. 实景三维中国建设布局与实现路径思考[J]. 测绘与空间地理信息, 2021, 44(7): 6-8, 14. Wang W, Wang C Y. Thoughts on real scene three-dimensional China construction layout and realization path [J]. Geomatics & Spatial Information Technology, 2021, 44(7): 6-8, 14. (in Chinese)
[2] 王树臻, 郑国强, 王光生, 等. 多源点云数据融合的建筑物精细化建模[J]. 测绘通报, 2020(8): 28-32, 38. Wang S Z, Zheng G Q, Wang G S, et al. Building fine modeling based on multi-source point cloud data fusion [J]. Bulletin of Surveying and Mapping, 2020(8): 28-32, 38. (in Chinese)
[3] 晏娅萍, 岳彩荣. 基于多源点云数据融合的单木树形重建[J]. 桂林理工大学学报, 2020, 40(3): 568-573. Yan Y P, Yue C R. Contour reconstruction of single tree based on multi-source point cloud data fusion [J]. Journal of Guilin University of Technology, 2020, 40(3): 568-573. (in Chinese)
[4] 丁祥, 龚坚刚, 周文俊, 等. 基于多源数据融合的输电杆塔三维重建[J]. 自动化技术与应用, 2022, 41(7): 110-113. Ding X, Gong J G, Zhou W J, et al. Three-dimensional reconstruction of transmission tower based on multi-source data fusion [J]. Techniques of Automation and Applications, 2022, 41(7): 110-113. (in Chinese)
[5] Garland M, Heckbert P S. Surface simplification using quadric error metrics [C]//Proceedings of the 24th Annual Conference on Computer Graphics and Interactive Techniques, 1997: 209-216.
[6] 刘峻, 范豪, 孙宇, 等. 结合边折叠和局部优化的网格简化算法[J]. 计算机应用, 2016, 36(2): 535- 540. Liu J, Fan H, Sun Y, et al. Mesh simplification algorithm combined with edge collapse and local optimization [J]. Journal of Computer Applications, 2016, 36(2): 535-540. (in Chinese)
[7] 张春森, 张会, 郭丙轩, 等. 城市场景结构感知的网格模型简化算法[J]. 测绘学报, 2020, 49(3): 334-342. Zhang C S, Zhang H, Guo B X, et al. Structure-aware simplified algorithm of mesh model for urban scene [J]. Acta Geodaetica et Cartographica Sinica, 2020, 49(3): 334-342. (in Chinese)
[8] 李鹏, 颜青松, 曲英杰, 等. 融合纹理信息的实景三维模型简化算法[J]. 测绘科学, 2021, 46(10): 151-158, 166. Li P, Yan Q S, Qu Y J, et al. Mesh simplified algorithm considering texture information[J]. Science of Surveying and Mapping, 2021, 46(10): 151-158, 166. (in Chinese)
[9] Wei Y G, Wu L, Sun B, et al. Improved QEM simplification algorithm based on discrete curvature and a sparseness coefficient [C]//2014 International Conference on IT Convergence and Security (ICITCS), 2014: 1-5.
[10] Trettner P, Kobbelt L. Fast and robust QEF minimization using probabilistic quadrics [J]. Computer Graphics Forum, 2020, 39(2): 325-334.
[11] Gao X F, Wu K, Pan Z R. Low-poly mesh generation for building models [C]//Special Interest Group on Computer Graphics and Interactive Techniques Conference, 2022: 1-9.
[12] Schönberger J L, Zheng E, Frahm J M, et al. Pixelwise view selection for unstructured multi-view stereo [C]//14th European Conference on Computer Vision (ECCV), 2016: 501-518.
[13] Zheng E L, Dunn E, Jojic V, et al. PatchMatch based joint view selection and depthmap estimation [C]//2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014: 1510-1517.
[14] 康志忠, 王薇薇, 李珍. 多源数据融合的三维点云特征面分割和拟合一体化方法[J]. 武汉大学学报(信息科学版), 2013, 38(11): 1317-1321, 1382. Kang Z Z, Wang W W, Li Z. An integrative method of 3D point clouds feature segmentation and fitting fusing multiple data sources [J]. Geomatics and Information Science of Wuhan University, 2013, 38(11): 1317-1321, 1382. (in Chinese)
[15] Lin Y B, Wang C, Zhai D W, et al. Toward better boundary preserved supervoxel segmentation for 3D point clouds [J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2018, 143: 39-47.
[16] 赖亦斌, 陆声链, 钱婷婷, 等. 植物三维点云分割[J]. 应用科学学报, 2021, 39(4): 660-671. Lai Y B, Lu S L, Qian T T, et al. Three-dimensional point cloud segmentation for plants [J]. Journal of Applied Sciences, 2021, 39(4): 660-671. (in Chinese)
[17] Besl P J, Jain R C. Segmentation through variable-order surface fitting [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1988, 10(2): 167-192.
[18] 李文, 刘德儿, 王有毅, 等. 基于超体素的区域聚类的复杂场景分割[J]. 激光与红外, 2021, 51(11): 1425-1432. Li W, Liu D E, Wang Y Y, et al. Complex scene segmentation based on supervoxel region clustering [J]. Laser & Infrared, 2021, 51(11): 1425-1432. (in Chinese)
[19] 韩英, 郑文武, 赵莎, 等. 一种改进的超体素与区域生长点云分割方法[J]. 测绘通报, 2022(12): 126-130. Han Y, Zheng W W, Zhao S, et al. An improved method for segmentation of supervoxel and regional growing point clouds [J]. Bulletin of Surveying and Mapping, 2022(12): 126-130. (in Chinese)
[20] 段黎明, 邵辉, 李中明, 等. 高效率的三角网格模型保特征简化方法[J]. 光学精密工程, 2017, 25(2): 460-468. Duan L M, Shao H, Li Z M, et al. Simplification method for feature preserving of efficient triangular mesh model [J]. Optics and Precision Engineering, 2017, 25(2): 460-468. (in Chinese)
[21] 兰峰, 张帆, 高云龙, 等. 顾及地物类别的倾斜摄影三维模型简化方法[J]. 测绘通报, 2022(4): 44-50. Lan F, Zhang F, Gao Y L, et al. A simplification method for 3D model of oblique photography considering geographic categories [J]. Bulletin of Surveying and Mapping, 2022(4): 44-50. (in Chinese)
[22] 张春森, 张萌萌, 郭丙轩. 影像信息驱动的三角网格模型优化方法[J]. 测绘学报, 2018, 47(7): 959-967. Zhang C S, Zhang M M, Guo B X. Refinement of the 3D mesh model driven by the image information [J]. Acta Geodaetica et Cartographica Sinica, 2018, 47(7): 959-967. (in Chinese)
[23] 杨煜, 冼楚华, 李桂清. 结合局部区域特征的自适应简化率网格简化算法[J]. 计算机辅助设计与图形学学报, 2020, 32(6): 857-864. Yang Y, Xian C H, Li G Q. Mesh simplification with adaptive simplified ratio based on local region features [J]. Journal of Computer-Aided Design & Computer Graphics, 2020, 32(6): 857-864. (in Chinese)
[24] Silva S, Madeira J, Santos B S. PolyMeCo-an integrated environment for polygonal mesh analysis and comparison [J]. Computers & Graphics, 2009, 33(2): 181-191.
[25] 李大军, 苟国华, 吴天辰, 等. 结构信息约束的三角网格模型简化方法[J]. 测绘科学, 2021, 46(8): 88-95, 164. Li D J, Gou G H, Wu T C, et al. Surface simplification method with structural information constraint [J]. Science of Surveying and Mapping, 2021, 46(8): 88-95, 164. (in Chinese)
Options
Outlines

/

〈 〉