散乱点云数据处理相关算法的研究
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摘要
随着三维激光扫描技术的发展,人们可以快速准确的获得物体表面大量的采样点。但是这些数据非常庞大,对后续的实时和高效的处理带来了很大的挑战,因此准确且高效的处理这些点云数据,并最终生成逼真的实物模型成为研究的一个重点。本文在此背景之下,对散乱点云数据处理的相关算法进行了研究,其主要研究内容有以下几个方面：
1.对空间包围盒分块中栅格边长的计算方法进行了改进。首先以给定边长进行首次划分,计算出划分栅格的黑体占有率；然后综合考虑数据集的范围、点的总数、最近点数k以及黑体占有率的情况下进行第二次划分,使得对不同点云数据的最佳边长计算更为精确。
2.在分析了基于点距与基于曲率的精简方法的基础上,综合考虑了数据简化速度与简化精度的前提下给出了一种基于向量夹角的数据简化算法。算法首先计算出采样点及其邻域点的重心点,再计算采样点指向邻域点的向量与采样点指向重心点的向量的夹角,取这些夹角的平均值作为采样点的夹角,依据采样点的夹角值大小来识别特征点；实验结果表明该算法策略能够较好的识别特征点,既提高了简化的速度,又很好的保持了物体表面的几何特征。
3.分析了各种已有散乱数据边界点提取算法。在此基础上根据边界点与其邻域点的分布特征,给出了一种基于点距的边界点快速提取算法。算法首先计算采样点及其邻域点的重心点,跟据采样点到重心点的距离与采样到最远邻域点的距离的比值来识别边界点。实验表明该算法能够快速准确的提取三维散乱数据点的边界点。
With the development of three-dimensional laser scanning technology, people can quickly and accurately get sample points from the surface of object; but these data points are very large, so it is an enormous challenge to deal with these points real-time and efficiently. Processing the points cloud accurately and efficiently and generating a realistic physical model ultimately will be a research focus. Dissertation studied the scattered point cloud processing and related technologies in this background, and the main research contents are as follow:
1. Space bounding box of side length blocking strategy estimation methods are improving. First of all, using a given side length for the first time to divide the grid and calculate the blackbody ratio; and then comprehensive consider the scope of data set, total number of points, k-nearest neighbors points and blackbody ratio for the second division, for a variety of point cloud data making estimate of side length more reasonable, the calculation more efficient.
2. Analyzing the methods of data simplification base on point distance and curvature, and then proposes a new algorithm for data simplification base on vector angle over considering the efficient and accuracy of data simplification; Firstly, calculate the center of gravity of sample point and the neighborhood points, and calculate the vectors of sample point to the neighborhood points and the vector of sample point to the center of gravity of points, then calculate the angle between vectors and take the average of these angles as the angle of sample point. According to the angle of the sample point to identify the feature point; The experiment result illustrate that the algorithm can discriminate the feature points and boundary points, keep the geometrical features and boundary points of the surface, and make data simplification more efficient.
3. Analyzing methods of data extraction from scattered data, and then proposing an algorithm for data extraction according to the characteristic of boundary points and nearest neighborhood points; Firstly, calculating the center of gravity of nearest neighborhood points, and then identifying the boundary points according to the ratio between the distance of the sample point to the center of gravity point and the distance of the sample point to the farest point of nearest neighborhood points.The experiment result illustrate that the algorithm can extract the boundary points more efficient and exactly.
1.对空间包围盒分块中栅格边长的计算方法进行了改进。首先以给定边长进行首次划分,计算出划分栅格的黑体占有率；然后综合考虑数据集的范围、点的总数、最近点数k以及黑体占有率的情况下进行第二次划分,使得对不同点云数据的最佳边长计算更为精确。
2.在分析了基于点距与基于曲率的精简方法的基础上,综合考虑了数据简化速度与简化精度的前提下给出了一种基于向量夹角的数据简化算法。算法首先计算出采样点及其邻域点的重心点,再计算采样点指向邻域点的向量与采样点指向重心点的向量的夹角,取这些夹角的平均值作为采样点的夹角,依据采样点的夹角值大小来识别特征点；实验结果表明该算法策略能够较好的识别特征点,既提高了简化的速度,又很好的保持了物体表面的几何特征。
3.分析了各种已有散乱数据边界点提取算法。在此基础上根据边界点与其邻域点的分布特征,给出了一种基于点距的边界点快速提取算法。算法首先计算采样点及其邻域点的重心点,跟据采样点到重心点的距离与采样到最远邻域点的距离的比值来识别边界点。实验表明该算法能够快速准确的提取三维散乱数据点的边界点。
With the development of three-dimensional laser scanning technology, people can quickly and accurately get sample points from the surface of object; but these data points are very large, so it is an enormous challenge to deal with these points real-time and efficiently. Processing the points cloud accurately and efficiently and generating a realistic physical model ultimately will be a research focus. Dissertation studied the scattered point cloud processing and related technologies in this background, and the main research contents are as follow:
1. Space bounding box of side length blocking strategy estimation methods are improving. First of all, using a given side length for the first time to divide the grid and calculate the blackbody ratio; and then comprehensive consider the scope of data set, total number of points, k-nearest neighbors points and blackbody ratio for the second division, for a variety of point cloud data making estimate of side length more reasonable, the calculation more efficient.
2. Analyzing the methods of data simplification base on point distance and curvature, and then proposes a new algorithm for data simplification base on vector angle over considering the efficient and accuracy of data simplification; Firstly, calculate the center of gravity of sample point and the neighborhood points, and calculate the vectors of sample point to the neighborhood points and the vector of sample point to the center of gravity of points, then calculate the angle between vectors and take the average of these angles as the angle of sample point. According to the angle of the sample point to identify the feature point; The experiment result illustrate that the algorithm can discriminate the feature points and boundary points, keep the geometrical features and boundary points of the surface, and make data simplification more efficient.
3. Analyzing methods of data extraction from scattered data, and then proposing an algorithm for data extraction according to the characteristic of boundary points and nearest neighborhood points; Firstly, calculating the center of gravity of nearest neighborhood points, and then identifying the boundary points according to the ratio between the distance of the sample point to the center of gravity point and the distance of the sample point to the farest point of nearest neighborhood points.The experiment result illustrate that the algorithm can extract the boundary points more efficient and exactly.
引文
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[2]Reeves W.T. Particle Systems-A Technique for Modeling a Class of Fuzzy Objects [J]. Computer Graphics,1983,17(3):359-376
[3]Levoy M, Whitted T. The Use of Points as Display Primitives [M]. The University of North Carolina at Chapel Hill, Department of Computer Science,1985:88-96
[4]Levoy M, Pulli K, Curless B, et al. The Digital Michelangelo Project:3D Scanning of Large Statues [J]. In:Proceedings of SIGGRAPH 2000:131-144
[5]Rusinkiewieez M, Levoy M. QsPlat:A Multire-solution Point Rendering System of Large Meshes[C]. In:Proceedings of SIGGRAPH 2000:343-352
[6]Dachsbacher C, Vogelgsang C. Marc Stamminger Sequential Point trees[C]. In: Proceedings of SIGGRAPH 2003:657-662
[7]Cohen J.D, Aligae D.G, Zhang W. Hybrid Simplification:Combining Multi-resolution Polygon and Point Rendering[C].In: Proceedings of IEEE Visualization 2001:37-44
[8]Stamminger M, Drettakis G. Interactive Sampling and Rendering for Complex and procedural geometry[C]. In:12th Euro graphics workshop on Rendering 2001:235-242
[9]Kalaiah A, Varshney. Differential Point Rendering Techniques[C]. In:21th Eurographics Workshop on Rendering 2001:138-150
[10]M. Zwicker, M. Pauly, O. Knoll et al. Pointshop 3D:an interactive system for point-based surface editing. SIGGRAPH'02,36(4):322-329
[11]S. Fleishman, Daniel C. O. M. Alexa et al. Progressive point set surfaces. ACM Transactions on Graphics,2002,20-36
[12]D. Lischinski and A. Rappoport. Image-based rendering for non-diffuse synthetic scenes. In Rendering Techniques'98, Springer, Wien, Vienna, Austria, June 1998,301-314
[13]周儒荣,张丽艳,苏旭,周来水.海量散乱点的曲面重建算法研究[J].软件学报,2001,12(2)：249-255
[14]马长胜,姜晓峰,强鹤群.散乱数据点的k近邻快速搜索算法[J].微电子学与计算机,2007,24(12)：206-209
[15]张丽艳,周儒荣,苏旭,周来水.海量测量数据简化技术研究[J].计算机辅助设计与图形学学报,2001,13(11)：1019～1023
[16]Qunsheng Peng, Wei Hua, Xuehui Yang. A New Approach of Point-Based Rendering[C]. In:Proceedings of Computer Graphics and Applications 2001:156-163
[17]缪永伟,工章野,彭群生等.点模型曲面的调和映射参数化[J].计算机辅助几何设计与图形学学报,2004,16(10)：1371～1375
[18]张龙,董朝,陈为,彭群生.大规模点模型的实时高质量绘制[J].计算机学报,2005,28(2)：241～249
[19]刘常青,彭翔,张宗华,胡小康.基于逆反工程(RE)多数据点云的处理[J].工程图学学报,2002,23(4)：33～93
[20]Levoy M, PulliK, Curless B, Rusinkiewicz S, Koller D, Pereira L, Ginzton M, Anderso S, Davis J, Ginsberg J, Shade J, Fulk D. The digital Michelangelo Projeet:3D scanning of large statues. Computer Graphics Proceedings[C], Annual Conferene Series, ACM SIGGRAPH, San Antonio, Texas, USA,2000:131-144
[21]Nirant V Puntambekar, Andrei G Jablokow, H Joseph Sommer III Unified review of 3D model generation for reverse engineering [J]. Computer Integrated Manufacturing System,1994,7(4):259-268
[22]A.Gruss, S. Tada and T. Kanade. A VLSI Smart Sensor for Fast Range Imaging[C]. IEEE International Conference on Intelligent Robots and Systems,1992,6(1):349-358
[23]D. Seharstein and R. Szeliski. A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. In International Journal of Computer Vision,2002,47(1): 37-42
[24]Nirant V Puntambekar, Andrei G Jablokow, H Joseph Sommer III Unified review of 3D model generation for reverse engineering[J]. Computer Integrated Manufacturing System,1994,7(4):259-268
[25]Varady T, Martin R.R, Cox J, Reverse engineering of geometric models-an introduction. CAD,1997,29(4)
[26]黄雪梅,王平江,陈洁红等.三维散乱三角形网格逼近的一种算法[J].计算机工程与设计,1998-19(2)：9-15
[27]李泽宇,李德华,胡汉平等.基于八叉树的三维散乱数据点的法矢的估计[J].计算机与数学工程,2000,28(4)：62～65
[28]张丽艳,周儒荣,苏旭等.海量散乱点的曲面重构算法研究[J].软件学报,2001,12(2)：249～255
[29]张丽艳,周儒荣,蔡炜斌等.海量测量数据简化技术研究[J].计算机辅助设计与图形学学报,2001,13(11)：1119～1023
[30]戴静兰.海量点云预处理算法研究[硕士学位论文].杭州：浙江大学[2006].
[31]Filip D, Magedson R, Markot R. Surface algorithms using bounds on derivatives [J]. Computer Aided Geometric Design,1986,3 (2):295-311
[32]Sun W, Bradley C, Zhang Y F, Cloud data modeling employing a unified non-redundant triangle mesh, Computer Aided Design,2001,33(2):183-193
[33]Weir DJ, Milroy M, Bradley C, Vickers GW, Reverse engineering physical models employing wrap-aroud B-spline surfaces and quadrics [A]. Pro Introduction Mech Engrs-PartB[C].1996,210:147-157
[34]Hameiri E, Shimshoni I. Estimation the principal curvatures and the Darboux frame from real 3-D range data [J]. IEEE Transaction on Systems, Man, and Cybernetics, Part B, 2003,33 (4):626-637
[35]Paul J B, Neil D M. A method for registration of 3D shapes [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,1992,14 (2):239-256
[36]HOPPE H, DEROSE T, DUCHAMP T. Surface reconstruction from unorganized points [J]. Computer Graphics,1992,26 (2):71-78
[37]GUO B, MENON J, W ILLETTE B. Surface reconstruction using alpha shapes [J]. Computer Graphics Forum,1997,16(4):177-190
[38]AMENTA N, BERNM, KAMVYSSEL ISM. A new voronoi-based surface reconstruction algorithm[C]. Proceeding of International Conference on Computer Graphics and Interactive Techniques 1998. New York:ACM Press,1998:415-421
[39]Bentley J L. Multidimensional binary search trees used for associative searching [J]. Communications of the ACM,1975,18 (9):509-517
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