全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...

一种基于3D点云的规则化模型自动构建方法
A Method of Building a Regularized Model Automatically Based on a 3D Point Cloud

DOI: 10.12677/GST.2021.93012, PP. 98-106

Keywords: 点云,滤波,平面分割,三角化,建模
Point Cloud
, Filter, Plane Segmentation, Triangulation, Modeling

Full-Text   Cite this paper   Add to My Lib

Abstract:

近年来人们对三维模型的需求与日俱增,三维激光点云技术的兴起为此提供了一个新的方向。然而,目前基于3D点云的建模工作大都通过人工交互的方式进行,因此如何利用3D点云数据实现三维模型的自动构建是目前研究的热点。本文提出一种基于3D点云的规则化模型自动构建方法:首先使用优化后的RANSAC算法对数据进行平面分割,然后提取出每个平面的轮廓线并进行分段;最后用线段中的关键点进行三角化,完成对三维模型的构建。实验结果表明,该方法可以完成独立平面和规则物体的三维模型自动构建,并可以灵活地与其他方法相结合以帮助构建物体中的规则部分,对规则物体的有较好的还原效果且所需三角面少。
In recent years, people’s demand for 3D models is increasing day by day, and the rise of 3D laser point cloud technology provides a new direction for this. However, most of the current modeling based on 3D point cloud is carried out through human interaction, so how to use 3D point cloud data to realize the automatic construction of 3D model is the focus of current research. This paper proposes a method of automatic construction of a regularized model based on 3D point cloud: first, the data is split flat using the improved RANSAC algorithm, then the outline of each plane is ex-tracted and segmented, and finally the three-dimensional model is constructed by triangulation by the key points in the segment. Experimental results show that the method can complete the auto-matic construction of three-dimensional models of independent planes and regular objects, and can be flexibly combined with other methods to help build the rule part of the object, which has a good reduction effect on the regular object and requires fewer triangular faces.

References

[1]  师顿. 基于TIN法向量的边缘检测与建筑物提取方法研究[D]: [硕士学位论文]. 西安: 西安电子科技大学, 2014.
[2]  Yang, Y., Fu, M., Wang, W., et al. (2010) 3D Laser Point Cloud-Based Navigation in Complex Environment. Control Conference (CCC), Beijing, China, 29 June 2010, 3798-3803.
[3]  周华伟, 朱大明, 瞿华蓥. 三维激光扫描技术与GIS在古建筑保护中的应用[J]. 工程勘察, 2011, 39(6): 73-77.
[4]  张坤. 基于三维激光扫描的点云数据逆向重建算法研究[D]: [博士学位论文]. 秦皇岛: 燕山大学, 2016.
[5]  张珍铭. 基于Delaunay三角化的三维散乱点云曲面重塑算法研究[D]: [硕士学位论文]. 南京: 南京航空航天大学, 2006.
[6]  刘涛. 泊松隐式曲面重建算法及其并行化研究[D]: [硕士学位论文]. 太原: 中北大学, 2018.
[7]  Nan, L.L. and Wonka, P. (2017) PolyFit: Polygonal Surface Reconstruction from Point Clouds. 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy, 22-29 October 2017, 2372-2380.
https://doi.org/10.1109/ICCV.2017.258
[8]  闫阳阳, 李永强, 王英杰, 李立雪, 吴珍珍. 三维激光点云联合无人机影像的三维场景重建研究[J]. 测绘通报, 2016(1): 84-87.
[9]  Fischler, M.A. and Bolles, R.C. (1981) Ran-dom Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography. Communications of the ACM, 24, 381-395.
https://doi.org/10.1145/358669.358692
[10]  Schnabel, R., Wahl, R. and Klein, R. (2007) Efficient RANSAC for Point-Cloud Shape Detection. Computer Graphics Forum, 26, 214-226.
https://doi.org/10.1111/j.1467-8659.2007.01016.x
[11]  董伟. 利用邻近点几何特征实现建筑物点云特征提取[J]. 激光与光电子学进展, 2018, 55(7): 175-182.
[12]  陈西江, 章光, 花向红. 于法向量夹角信息熵的点云简化算法[J]. 中国激光, 2015, 42(8): 336-344.
[13]  Rusu, R.B. (2010) Semantic 3D Object Maps for Everyday Manipulation in Human Living Environments. Künstliche Intelligenz, 24, 345-348.
https://doi.org/10.1007/s13218-010-0059-6
[14]  巩丹超, 戴晨光, 张永生. 三维模型重建中的凹多边形三角剖分[J]. 解放军测绘学院学报, 1999, 16(3): 40-42.

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133