基于地面激光扫描点云数据的三维重建方法研究
|
摘要
随着地面激光扫描技术的日臻成熟并被逐步应用于地物三维重建工作,地面激光点云正成为重建工作中的重要基础数据。作为一种直接对目标表面进行三维测量的技术,地面激光扫描系统能够以阵列式点云的形式描绘地物表面的空间形态和记录点位坐标信息,并且可以根据激光束回波反射强度值以及融合CCD影像的色彩信息,使得点云数据不仅具有空间几何特征,同时还包含有地物表面的光谱辐射信息。通过建立点云数据的邻域关系和目标点云表面的拓扑几何特征,实现以网格或参数曲面的形式构建地物的表面几何模型；而光谱辐射信息则被用于恢复几何模型表面的色彩或纹理特征。联合光谱辐射信息的数据处理方法是当前目标重建技术中的一个研究热点,通过在基于几何特征的数据处理算法中增加光谱辐射信息这一辅助阈值条件,用以改善重建算法的稳健性并提高重建模型的质量。本文在分析已有地面激光点云数据处理方法的基础上,对地物三维重建工作中的网格点云数据的配准、点云数据的分割、点云模型的轮廓提取、点云模型的纹理映射等关键问题进行了深入研究,并尝试从融合光谱辐射信息的角度实现对现有数据处理算法进行改进,提高算法的适用性和敏感度。本文在对地面激光扫描仪的系统组成、扫描作业步骤、地面点云数据的邻域划分和拓扑几何特征关系、以及点云数据中的光谱辐射信息等内容进行充分论述的基础上,着重对重建工作中的以下问题进行了详细研究：
(1)针对同名特征关系在配准工作中不易自动建立的问题,以特征点集的自动搜索为研究内容,结合实际工作中三角网格被广泛应用于点云建模这一工作环境,提出了一种基于网格顶点特征的点云自动配准算法：①以三角网格的顶点曲率为研究对象,用基于邻域平均度量的曲率计算方法进行特征点集的筛选,提高了曲率计算的可靠性；通过交互式的方法使待配准点云的初始位置指向网格的同一方向,降低了预配准计算的复杂度。②依托k-d搜索树建立的邻域关系,从特征点集中搜索特征面片,以三角面片的顶点作为预配准的初始估值,然后进行ICP法的配准计算,经实例验证,该方法搜索准确,配准结果可靠,具有一定的工程实用价值。③对于某些特殊条件下,曲率计算有可能失效的情况,给出了基于网格顶点光谱特征的特征点集筛选模型,能够在一定程度上对原有几何特征配准算法进行补充。
(2)首先对已有的点云几何分割算法进行了回顾,分析了将回波反射强度信息作为辅助条件引入到点云平面分割算法的可行性。针对平面在建筑物立面中广泛出现的特点,根据激光回波反射强度与地物反射特性具有相关性的特点,改进了平面生长的点云分割算法,提出在生长阈值条件中增加反射强度值的相关性约束条件,同时引入随机一致性算法改善初始面片模型参数估计的稳健性,提高了平面生长分割算法的可靠性。实验说明,对于一定条件下获得地形激光点云而言,使用强度值辅助的点云分割算法具有两面性,算法灵敏度的提高一方面会导致面片的过于细分,产生不应有的得多余分割面片,但另一方面,却也可以提高算法对地物特征的识别能力,对于目标表面细节的层次特征提取具有一定的促进意义。
(3)在分析了建筑物立面点云的典型结构特征、几何特点的基础上,讨论了建筑物立面模型快速重建中激光扫描策略的改进方法,然后以点云数据转换为轮廓线模型为重建工作的主要研究内容,提出了针对点云密度不均匀的建筑物立面内、外轮廓的提取作业方法,考虑到点云密度对不同边界提取算法所造成的影响,对边界跟踪和空间网格划分两种方法进行了混合使用。利用极值点确定外轮廓种子点,然后进行边界跟踪获取一定数量的边界点；利用空间网格划分的方法对内部空洞区域进行探测,确定内轮廓的初始点集。在对边界点集进行直线段拟合的基础上,针对内轮廓点集不能真实反映立面特征,如窗户真实尺寸的问题,认为需要结合实测数据进行边界的修正。最后,再进行边界的规则化处理,从而实现点云立面轮廓线的建立。
(4)从场景光照一致性的角度对数字影像匀光的必要性和匀光模型的选择进行了研究分析,并实验利用Wallis滤波器对具有一定重叠度的数字影像进行匀光处理,使两幅影像在色彩空间的各个通道上趋于一致；然后通过分析影像的畸变模型和数字影像的几何校正模型,并根据数字影像成像时相机、激光扫描仪扫描中心与物方的相对位置关系,研究了摄影中心线与扫描中心线夹角在不同情况下的两种映射模型,对于小倾角的情况,认为可以用直接线性变换的方法实现点云模型到纹理影像的映射过程,并直接利用点云坐标进行相机内方位元素的解算；对于大倾角的情况,则认为需要通过的相机的标定,确定内方位元素,然后在利用后方交会的严密模型进行点云模型到影像的映射工作。通过以上研究,使得从三维点云到二维单片影像的纹理映射方法在实际应用中更加具有针对性。最后针对纹理建模中的点云数据和点云网格模型分别进行了基于三维直接线性变换的单片纹理映射和基于后方交会直接解的多视点纹理重复映射实验。
融合几何拓扑特征与光谱辐射信息的数据处理方法,在基于地面激光点云数据的重建工作中有着巨大的应用潜力。本文在对地物三维重建中几项关键问题进行研究的过程中,对将光谱辐射信息用以改善重建模型质量的方法进行了分析,取得了一定成果,最后给出了全文的总结。
Terrestrial laser scanning technology, to which lots of improvements and breaking-throughs have been contributed, has gradually won more and more cases when applied to object reconstruction, and raises as as one of the fundemental data of the reconstruction works. By recording data like amplitudes of return waves, and colors of fusioned CCD images, this new terrestrial laser scanning technology retrieves information of the radiation properties of the surfaces of the objects, while traditionally only geospatial attributions could be distrilled from the array-like point clouds with 3D position coordinates. The geospatial data are always used to construct the neighboring relationships of points and topological natures of surfaces of the targets, based on which surface geometrical modeling could be built either as grids or as parametric curvatures. Meanwhile the radiation information are employed to recover the color and texture features of the models. Ever since the first introduction of augmented algorithms with auxillary threshholds provided by extra radiation conditions, the synthetic process has attracted quite a lot research effort and concerns, in the hope of achieving better robustness. After a well-structured review on classical theories and algorithms of key problems of terrestrial laser scanning, including matching, segmentation, outline extracting and texture mapping of the productions of the point cloud data, this paper, utilizing radiation information, makes several tries to improve ability of the algorithms, like adapting to larger application fields and stay sensitive even when dealing with tough data sets. Beside detailed discussion about system structure, scanning process, building neighboring relationship and topological nature, abd radiation data of the terrestial laser system, this paper concentrates on the following topics:
(1) In a background that trigonal grids have been broadly accepted in everyday work, and aiming at difficulties in establishing homogeneous feature relationship, a new grid-feature based, auto-registration algorithm is proposed. Several remarks on this algorithm are:1. It filters featured point sets using neighboring average metric curvature method, which comes from properties of vertex curvature of trigonal grids, and resulting in more reliable curvatures; also, it forces the initial orientation of the clouds to the same direction interactively, and receives reduction of computational complexity of pre-registration.2. It engineeringly proves that applying ICP over pre-registration data sets, which are generated from vertex of selected trigonal patches computed from k-d tree, would contribute to a better registration.3. In order to avoid odd situations that curvature computation would fail occasionally, it sets up a filtering model according to radiation attributions of grid vertex, and eventually serves as a backup stradegy.
(2) Starting with analysis of present segmentation algorithms, this paper focuses on the feasibility of hypothesis that additional conditions, namely amplitudes of return waves, would work with segmentation methods and enhence the output. In a knowledge that flat planes are popular in building facades and there is a apparent relationship between object reflection attributions and amplitudes of return waves, an improved plane-growing algorithm, which refines growing threshhold conditions also in consideration of amplitudes of return waves, is designed and furthermore, RANSAC is used to get better robustness on parametric estimation of initial patch models. Moreover, the corresponding tests prove that to point clouds under some specific conditions, modified method would cause oversegmentation and leaves with residue patches, however, it does contribute to improvements of recognition of object features as well.
(3) After analyzing the classical structural and geometrical features of building facade's point clouds, this paper discusses improved scanning strategy in fast rebuilding of building facades. Then, it focuses on methods that transform point clouds to outline models, and proposes job procedures on extract inner and outer outlines from nonuniformly distributed point clouds data, with combined methods of both border trace and spatial grids, due to effects on different methods caused by unbalanced data. This new method determines outline seed by local maximum and traces the border at a specific level; also, with the help of spatial grids, it detects the vacuums and chooses initial point set of inner outline. Because of inner outline cannot sincerely reflect features of facades, for example, the real size of window, this paper first fits the segments of the border point sets, and then correct the borders with measure data. Finally, it normalizes the border to get the outlines.
(4) From coherence of scene lighting, this paper details on the necessarity of image dodging and how to choose dodging models. In this section, a series of overlapped images are dodged with Wallis filter, and coherence is archived on every color band of a pair of images. Accompanying with distortion models, geometric correction models, relative positions of camera, laser scanner and objects, two different projection models of the angle between camera and laser scanner are talked about. To the small angle situation, DLT method is accepted to transform point clouds to texture images, and inner orientation elements are solved directly with point clouds'coordinates. To the large angle situation, camera calibration is demanded and projection should be coped with strict model of resection method. In an conclusion, texture projections of above two models have been applied to several test data, and archived an improved result.
The data processing method by integration of geometric topology information and spectral radiation data has great potential applications in the reconstruction work based on terrestrial laser point cloud data. Through studying several key issues on object 3D reconstruction, achieve certain results by analyzing the methods about using spectral radiation model to improve the quality of reconstruction quality and give a summary of the full text at last.
(1)针对同名特征关系在配准工作中不易自动建立的问题,以特征点集的自动搜索为研究内容,结合实际工作中三角网格被广泛应用于点云建模这一工作环境,提出了一种基于网格顶点特征的点云自动配准算法：①以三角网格的顶点曲率为研究对象,用基于邻域平均度量的曲率计算方法进行特征点集的筛选,提高了曲率计算的可靠性；通过交互式的方法使待配准点云的初始位置指向网格的同一方向,降低了预配准计算的复杂度。②依托k-d搜索树建立的邻域关系,从特征点集中搜索特征面片,以三角面片的顶点作为预配准的初始估值,然后进行ICP法的配准计算,经实例验证,该方法搜索准确,配准结果可靠,具有一定的工程实用价值。③对于某些特殊条件下,曲率计算有可能失效的情况,给出了基于网格顶点光谱特征的特征点集筛选模型,能够在一定程度上对原有几何特征配准算法进行补充。
(2)首先对已有的点云几何分割算法进行了回顾,分析了将回波反射强度信息作为辅助条件引入到点云平面分割算法的可行性。针对平面在建筑物立面中广泛出现的特点,根据激光回波反射强度与地物反射特性具有相关性的特点,改进了平面生长的点云分割算法,提出在生长阈值条件中增加反射强度值的相关性约束条件,同时引入随机一致性算法改善初始面片模型参数估计的稳健性,提高了平面生长分割算法的可靠性。实验说明,对于一定条件下获得地形激光点云而言,使用强度值辅助的点云分割算法具有两面性,算法灵敏度的提高一方面会导致面片的过于细分,产生不应有的得多余分割面片,但另一方面,却也可以提高算法对地物特征的识别能力,对于目标表面细节的层次特征提取具有一定的促进意义。
(3)在分析了建筑物立面点云的典型结构特征、几何特点的基础上,讨论了建筑物立面模型快速重建中激光扫描策略的改进方法,然后以点云数据转换为轮廓线模型为重建工作的主要研究内容,提出了针对点云密度不均匀的建筑物立面内、外轮廓的提取作业方法,考虑到点云密度对不同边界提取算法所造成的影响,对边界跟踪和空间网格划分两种方法进行了混合使用。利用极值点确定外轮廓种子点,然后进行边界跟踪获取一定数量的边界点；利用空间网格划分的方法对内部空洞区域进行探测,确定内轮廓的初始点集。在对边界点集进行直线段拟合的基础上,针对内轮廓点集不能真实反映立面特征,如窗户真实尺寸的问题,认为需要结合实测数据进行边界的修正。最后,再进行边界的规则化处理,从而实现点云立面轮廓线的建立。
(4)从场景光照一致性的角度对数字影像匀光的必要性和匀光模型的选择进行了研究分析,并实验利用Wallis滤波器对具有一定重叠度的数字影像进行匀光处理,使两幅影像在色彩空间的各个通道上趋于一致；然后通过分析影像的畸变模型和数字影像的几何校正模型,并根据数字影像成像时相机、激光扫描仪扫描中心与物方的相对位置关系,研究了摄影中心线与扫描中心线夹角在不同情况下的两种映射模型,对于小倾角的情况,认为可以用直接线性变换的方法实现点云模型到纹理影像的映射过程,并直接利用点云坐标进行相机内方位元素的解算；对于大倾角的情况,则认为需要通过的相机的标定,确定内方位元素,然后在利用后方交会的严密模型进行点云模型到影像的映射工作。通过以上研究,使得从三维点云到二维单片影像的纹理映射方法在实际应用中更加具有针对性。最后针对纹理建模中的点云数据和点云网格模型分别进行了基于三维直接线性变换的单片纹理映射和基于后方交会直接解的多视点纹理重复映射实验。
融合几何拓扑特征与光谱辐射信息的数据处理方法,在基于地面激光点云数据的重建工作中有着巨大的应用潜力。本文在对地物三维重建中几项关键问题进行研究的过程中,对将光谱辐射信息用以改善重建模型质量的方法进行了分析,取得了一定成果,最后给出了全文的总结。
Terrestrial laser scanning technology, to which lots of improvements and breaking-throughs have been contributed, has gradually won more and more cases when applied to object reconstruction, and raises as as one of the fundemental data of the reconstruction works. By recording data like amplitudes of return waves, and colors of fusioned CCD images, this new terrestrial laser scanning technology retrieves information of the radiation properties of the surfaces of the objects, while traditionally only geospatial attributions could be distrilled from the array-like point clouds with 3D position coordinates. The geospatial data are always used to construct the neighboring relationships of points and topological natures of surfaces of the targets, based on which surface geometrical modeling could be built either as grids or as parametric curvatures. Meanwhile the radiation information are employed to recover the color and texture features of the models. Ever since the first introduction of augmented algorithms with auxillary threshholds provided by extra radiation conditions, the synthetic process has attracted quite a lot research effort and concerns, in the hope of achieving better robustness. After a well-structured review on classical theories and algorithms of key problems of terrestrial laser scanning, including matching, segmentation, outline extracting and texture mapping of the productions of the point cloud data, this paper, utilizing radiation information, makes several tries to improve ability of the algorithms, like adapting to larger application fields and stay sensitive even when dealing with tough data sets. Beside detailed discussion about system structure, scanning process, building neighboring relationship and topological nature, abd radiation data of the terrestial laser system, this paper concentrates on the following topics:
(1) In a background that trigonal grids have been broadly accepted in everyday work, and aiming at difficulties in establishing homogeneous feature relationship, a new grid-feature based, auto-registration algorithm is proposed. Several remarks on this algorithm are:1. It filters featured point sets using neighboring average metric curvature method, which comes from properties of vertex curvature of trigonal grids, and resulting in more reliable curvatures; also, it forces the initial orientation of the clouds to the same direction interactively, and receives reduction of computational complexity of pre-registration.2. It engineeringly proves that applying ICP over pre-registration data sets, which are generated from vertex of selected trigonal patches computed from k-d tree, would contribute to a better registration.3. In order to avoid odd situations that curvature computation would fail occasionally, it sets up a filtering model according to radiation attributions of grid vertex, and eventually serves as a backup stradegy.
(2) Starting with analysis of present segmentation algorithms, this paper focuses on the feasibility of hypothesis that additional conditions, namely amplitudes of return waves, would work with segmentation methods and enhence the output. In a knowledge that flat planes are popular in building facades and there is a apparent relationship between object reflection attributions and amplitudes of return waves, an improved plane-growing algorithm, which refines growing threshhold conditions also in consideration of amplitudes of return waves, is designed and furthermore, RANSAC is used to get better robustness on parametric estimation of initial patch models. Moreover, the corresponding tests prove that to point clouds under some specific conditions, modified method would cause oversegmentation and leaves with residue patches, however, it does contribute to improvements of recognition of object features as well.
(3) After analyzing the classical structural and geometrical features of building facade's point clouds, this paper discusses improved scanning strategy in fast rebuilding of building facades. Then, it focuses on methods that transform point clouds to outline models, and proposes job procedures on extract inner and outer outlines from nonuniformly distributed point clouds data, with combined methods of both border trace and spatial grids, due to effects on different methods caused by unbalanced data. This new method determines outline seed by local maximum and traces the border at a specific level; also, with the help of spatial grids, it detects the vacuums and chooses initial point set of inner outline. Because of inner outline cannot sincerely reflect features of facades, for example, the real size of window, this paper first fits the segments of the border point sets, and then correct the borders with measure data. Finally, it normalizes the border to get the outlines.
(4) From coherence of scene lighting, this paper details on the necessarity of image dodging and how to choose dodging models. In this section, a series of overlapped images are dodged with Wallis filter, and coherence is archived on every color band of a pair of images. Accompanying with distortion models, geometric correction models, relative positions of camera, laser scanner and objects, two different projection models of the angle between camera and laser scanner are talked about. To the small angle situation, DLT method is accepted to transform point clouds to texture images, and inner orientation elements are solved directly with point clouds'coordinates. To the large angle situation, camera calibration is demanded and projection should be coped with strict model of resection method. In an conclusion, texture projections of above two models have been applied to several test data, and archived an improved result.
The data processing method by integration of geometric topology information and spectral radiation data has great potential applications in the reconstruction work based on terrestrial laser point cloud data. Through studying several key issues on object 3D reconstruction, achieve certain results by analyzing the methods about using spectral radiation model to improve the quality of reconstruction quality and give a summary of the full text at last.
引文
[1]. Alliez, P. and Cohen-Steiner, D. and Tong, Y. and Desbrun, M. Voronoi-based variational reconstruction of unoriented point sets, Proceedings of the fifth Eurographics symposium on Geometry processing,p.48,2007, Eurographics Association.
[2]. A Ruiz, W Kornus, J Talaya, JL Colomer. TERRAIN MODELING IN AN EXTREMELY STEEP MOUNTAIN:A COMBINATION. OF AIRBORNE AND TERRESTRIAL LIDAR-Proceedings of XX congress of International Society,2006.
[3]. Abdelhafiz, A., Riedel, B. and Niemeier, W.,2005. Towards a 3D True Colored Space by the Fusion of Laser Scanner Point Cloud and Digital Photos. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol.XXXVI, part5/W17, Venice, Italy.
[4]. Abmayr T., Hartl F., Reinkoster M., Frohlich C. Terrestrial laser scanning-applications in cultural heritage conservation and civilengineering. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences,Volume XXXVI, Part 5/W17, ISSN 1682-1777 (2005).
[5]. Akca, D.,2004. Least squares 3D curve matching. Internal Technical Report at IGP-ETH, Zurich, March,27 pages.
[6]. Akca, D.,2007. Least squares matching of 3D surfaces.4th Symposium of Turkish Society for Photogrammetry and Remote Sensing, Istanbul, Turkey, June 5-7, (only on CD-ROM).
[7]. Akca, D. Least Squares 3D Surface Matching, Ph.D dissertation, Swiss Federal Institute of Technology Zurich,2007.
[8]. Alan Watt.3D Computer Graphics, Third Edition,2005.
[9]. Ameri, B. (2000). AUTOMATIC 3D BUILDING RECONSTRUCTION USING PLANE-ROOF STRUCTURES. ASPRS, Washington DC.
[10].Arun KS, Huang TS, Blostein SD. Least square fitting of two 3D point sets. IEEE Trans Pattern Anal Mach Intell 1987;9:698-700.
[11].Balis, V, Karamistos, S., Kotsis, I., Liapakis, C. And Simpas, N.,2004.3D Laser Scanning: Integration of Point Cloud and CCD Camera Video Data for the Production of High Resolution and Precision RGB Textured Models:Archaeological Monuments Surveying Application in Ancient India. In Proceedings of FIG Working Week, Athens, Greece, May 22-27.
[12].Besl,P.J., and Mckay, N.D., A method for registration of 3D shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence 14(2),239-256,1992.
[13].Boissonnat J.D. Geometric structures for three-dimensional shape representation[J]. ACM Trans Graphics,1984, V3(4):266-286.
[14].Bretar, F. and Roux, M.,2005. Hybrid image segmentation using LIDAR 3D planar primitives. International Archieves of Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XXXVI, part3/W19, Enschede, the Netherlands, pp,232.
[15].Briese C.2006:Structure line modelling based on terrestrial laserscanner data, ISPRS Symposium, Dresden, Commission V-Image Engineering and Vision Metrology.
[16].Brinkhoff, T.,2004. Spatial access methods for organizing laser scanner data. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences 35(B4),98-102.
[17].Bruno Caprile, Vincent Torre:Using vanishing points for camera calibration. International Journal of Computer Vision 4(2):127-139 (1990).
[18].Callieri, M. and Cignoni, P. and Corsini, M. and Scopigno, R. Masked photo blending: Mapping dense photographic data set on high-resolution sampled 3D models, Computers\& Graphics, Volume32,No.4,p464-473,2008.
[19].Chen Y, Medioni G Object modeling by registration of multiple range images[A]. Proc IEEE Int'l conf on Robotics and Automation[C],1992,2724-2729.
[20].Cheng Qian Fengting Li Chenghui Ge. Feature extraction from range images in 3D modeling of urban scenes, Robotics, Intelligent Systems and Signal Processing,2003. Proceedings.2003 IEEE International Conference, Volume:2, On page(s):909-914 vol.2.
[21].Dan Sunday. Intersection of line, segment, and plane in 2D and 3D,2001, http://www.softsurfer.com.
[22].David A. Forsyth and Jean Ponce. Computer Vision, a modern approach. Prentice Hall. ISBN ISBN 0-13-085198-1(2003).
[23].Desbrun M.Meyer M.Schroder P Discrete differential-geometry operators for triangulated 2-manifolds 2002.
[24].Ed Catmull, A Subdivision Algorithm for Computer Display of Curved Surfaces, PhD Thesis, University of Utah,1974.
[25].Evvgeny Lomonosov, Dmitry Chetverikov. Pre-registration of arbitrarily oriented 3D surfaces using a genetic algorithm, Computer and Automation Research Institute Budapest, 2005, Kende u.13-17, H-1111.
[26].Feng, Q.H. and K.Roshoff.,2004. In-situ mapping and documentation of rock faces using a full-coverage 3D laser scanning technique. International Journal of Rock Mechanics and Mining Sciences,41(Supplement 1):p.139-144.
[27].Filin,S.,2002. Surface clustering from airborne laser scanning data. International Archieves of Photogrammetry, Remote Sensing and Spatial Information Sciences, vol.XXXIV, part3A/B, Graz, Austria, pp.119-124.
[28].Forkert, G., Kerschner, M., Prinz, R., and Rottensteiner, F.,1995. Reconstruction of free-formed spatial curves from digital images. International Archives of Photogrammetry and Remote Sensing 30(5/W1),163-168.
[29].Gielsdorf,F.,A.Rietdorf, L.Gruending:A concept for the calibration of terrestrial laser scanners. In:Proc.of FIG Working Week, Athens,Greece,2004.
[30].Goldman, Ronald N. Intersection of three planes. In Andrew Glassner, editor, Graphics Gems, Academic Press, San Diego, page 305,1990.
[31].Gorte, B.G.H.,2002. Segmentation of tin-structured surface models, Joint Conference on Geo-spatial theory, Processing and Applications, Ottawa, Canada,8-12 July 2002.
[32].Gruen, A.,1985. Adaptive least squares correlation:a powerful image matching technique. South Africa Journal of Photogrammetry, Remote Sensing and Cartography 14(3),175-187.
[33].Gruen, A., and Li, H.,1996. Linear feature extraction with LSB-Snakes from multiple images. International Archives of Photogrammetry and Remote Sensing 31(3B),266-272.
[34].Hom B K P.Closed-form solution of absolute orientation using unit quaternions[J]. Journal of the Optical Society of America,1987,4(4):629-642.
[35].Hoover A.Gillian J B.Jiang Xiao-yi. An experimental comparison of range image segmentation algorithms, IEEE Transactions on Pattern Analysis and Machine Intelligence archive,1996(7).
[36].Reference from http://en.wikipedia.org/wiki/Kd-tree.
[37].I.Jazayeri, C.S.Fraser. Interest Operators in Close-Range Object Reconstruction,2008, http://www.isprs.org/proceedings/XXXVII/congress/5_pdf/12.pdf.
[38].Ioannidis, Ch., Demir, N., Soile, S., Tsakiri, M.. Combination of Laser Scanner Data and Simple Photogrammetric Procedures for Surface Reconstruction of Monuments, The CIPA International Archives forDocumentation of Cultural Heritage Volume XX-2005, pp.372-377 (2005).
[39]. J Bohm, N Haala. Efficient Integration of Aerial and Terrestrial Laser Data for Virtual City Modeling Using LASERMAPS-Proceedings of the ISPRS Workshop, Laser scanning,2005-elib.uni-stuttgart.de.
[40].J. Bohm, N. Haala. Efficient integration of aerial and terrestrial laser data for virtual city modeling using lasermaps in:IAPRS VOLUME XXXVI, part3/w19,2005.
[41].Jiang,X.Y.[Xiao-Yi],Bunke,H.,Meier,U.,High-level feature based range image segmentation, IVC(18), No.10, July 2000, pp.817-822.
[42].Jokinen, O.,1998. Area-based matching for simultaneous registration of multiple 3-D profile maps. Computer Vision and Image Understanding 71(3),431-447.
[43].Kazhdan, M. and Bolitho, M. and Hoppe, H. Poisson surface reconstruction, Proceedings of the fourth Eurographics symposium on Geometry processing,p.70,2006, Eurographics Association.
[44].Laura Chasmer, Chris Hopkinson, and Paul Treitz. Investigating laser pulse penetration through a conifer canopy by integrating airborne and terrestrial lidar, Canadian Journal of Remote Sensing, volume32, number2, Apirl 2006.
[45].Lee,I.,Schenk, T.,2001.3D perceptual organization of laser altimetry data. International Archives of Photogrammetry, Remote Sensing and Spatial Information Science 34(Part 3/W4),57-65.
[46].Lindenbergh, R., N. Pfeifer:A statistical deformation analysis of two epochs of terrestrial laser data of a lock. In Proc. of Optical 3D Measurement Techniques, Vol II, pp 61-70, Vienna, Austria,2005.
[47].Lionel Penard.3D Building Fa?ade Reconstruction under Mesh Form from Multiple Wide Angle Views, www.isprs.org/commission5/3darch05/pdf/17.pdf.
[48].Liu, Land Yu, Gand Wolberg, Gand Zokai,S. Multiview Geometry for Texture Mapping 2D Image s onto 3D Range Data,2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Volum2,2006.
[49].M J Milroy. Segmentation of a wrap-around model using an active contour, Computer-Aided Design, Volume 29, Number 4, April 1997, pp.299-320(22)
[50].M.A. Fischler and R. C. Bolles (June 1981).""Random Sample Consensus:A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography"". Comm. of the ACM 24:381--395. doi:10.1145/358669.358692.
[51].O.D.Faugeras and M.Hebert. The representation, recognition and locating of 3D objects, Int.J.Robotics Res.5,1986,27-52.
[52].Overby,J., Bodum, L., Kjems, E. and Ilsoe, P.M. Automatic 3D Building Reconstruction from Airborne Laser Scanning and Cadastral Data Using Hough Transform. International Archieves of Photogrammetry, Remote Sensing and Spatial Information Science, vol.XXXV,
part B3, Istanbul,Turkey,2004.
[53].Penard, L., Paparoditis, N., Pierrot-Deseilligny, M.,2005.3D building fa?ade reconstruction under mesh form from multiple wide angle views. In:Proceedings of International Workshop "3D-ARCH 2005", Mestre-Venice, Italy, IAPRS, vol. ⅩⅩⅩⅥ, part 5/W17, http://www.commission5.isprs.org/3darch05/pdf/17.pdf.
[54].Philip J. Schneider, David H.Eberly.'Geometric Tools for Computer Graphics', Elsevier Science(USA),2005.
[55].Piegl, Les, and Wayne Tiller.1995, The NURBS BOOK. Springer-Verlag, Berlin.
[56].Pottmann, H., Leopoldseder, S., and Hofer, M.,2004. Registration without ICP. Computer Vision and Image Understanding 95(1),54-71.
[57].Pu,S., Vosselman,G..Automatic extraction of building features from terrestrial laser scanning, International Archieves of Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol.36, part 5, Dresden, Germany, September 25-27,2006.
[58].Punya Prasad Sapkota. Segmentation of Coloured Point Cloud Data, ITC MSC Thesis, Enschede,the Netherlands,2008.
[59].Q. Huang and B. Dom, ""Quantitative methods of evaluating image segmentation,"" ICIP, vol.3, p.3053,1995.
[60].Qingming Zhan, Yubin Liang, Yinghui Xiao. Color-Based Segmentation of Point Clouds, http://www.isprs.org/proceedings/ⅩⅩⅩⅧ/3-W8/papers/p65.pdf.
[61].Rabbani,T., van den Heuvel, F.A and Vosselman, M.G.,2006. Segmentation of point clouds using smoothness constraints. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. ⅩⅩⅩⅥ, part 5, Dresden, Germany, pp.248-253.
[62].Remondino,F.2006, Image-based modelling for object and human reconstruction.PHD dissertation, Institute of Geodesy and Photogrammetry, ETH Zurich, Mitteilungen No.91,174pp.
[63].Richard Hartley and Andrew Zisserman. Multiple View Geometry in Computer Vision,2nd edition, Cambridge University Press(2003).
[64].Rogers, D.F., and Adams, J.A.,1976. Mathematical elements for computer graphics. McGraw-Hill Book Company, London, pp.116-155.
[65].Roggero,M.,2002. Object segmentation with region growing and principle component analysis. International Archives of Photogrammetry, Remote Sensing and Spatial Information Science 34(Part 3A),289-294.
[66].S. Li and D. Zhao, Gradient-based polyhedral segmentation for range images, Pattern Recognition Letters 24 (2003) (12), pp.2069-2077.
[67].S.Ferrari, G.Ferrigno, V.Piuri, N.A. Borghese.(2007) Reducing and Filtering Point Clouds with Enhanced Vector Quantization. IEEE Trans. On Neural Networks. Volume 18, Issue 1, Jan.2007 Pages:161-177.
[68].Schnabel, R., Wahl, R. and Klein, R.,2007b. Shape Detection in Point Clouds. Computer Graphics Technical report, No. CG-2006-2, University of Bonn, Germany.
[69].Shahar Barnea, Sagi Filin. Keypoint based autonomous registration of terrestrial laser point-clouds. ISPRS Journal of Photogrammetry& Remote Sensing 63,19-35 (2008).
[70].Sithole, G.,2005. Segmentation and classification of airborne laser scanning data. Ph.D Thesis, Technical university of Delft.
[71].Skarbek, W., A. Koschan,1994. Color Image Segmentation. Report, Technical University, Berlin.
[72].Slob, S. and Hack, R.:3-D Terrestrial Laser Scanning as a New Field Measurement and Monitoring Technique, in:Engineering Geology for Infrastructure Planning in Europe, A European Perspective, edited by:Hack, R., Azzam, R., and Charlier, R., Lecture Notes in Earth Sciences, Springer, Berlin/Heidelberg,104,179-190,2004.
[73].Talaya,J.,Bosch,E.,Serra,A.,Alamus,R.,Bosch,E.,Kornus,W.,2004b."Integration of a Terrestrial Laser Scanner with GPS/IMU Orientation Sensors.", International Archives of Photogrammetry and Remote Sensing,2004, Istambul, Turkey.
[74].Tao, V.C..3D Data Acquisition and Object.Reconstruction for AEC and CAD. In:S. Zlatanova and D. Prosperi (Editors), Large-scale 3D Data.Integration. CRCpress, Boca Raton, Florida, USA,pp.245 (2005).
[75].Tarsha-Kurdi, F., Landes, T. and Grussenmeyer, P.,2007. Hough-Transform and Extended RANSAC Algorithms for Automatic Detection of 3D Building Roof Planes from LIDAR Data. International Archieves of Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. ⅩⅩⅩⅥ, part3/W52, Espo, Finland, pp.407-412.
[76].TATIANA S.EVGENY M.OCTAVIAN S.A comparison of Gaussian and mean curvatures estimation methods on triangular meshes,2003.
[77].T6vari, D.,2006. Segmentation based classification of airborne laser scanner data. Ph.D Thesis, university Karlsruhe, Germany.
[78].Turk, G., and Levoy, M.,1994. Zippered polygon meshes from range images. Proc. of ACM SIGGRAPH'94, Orlando(Florida), July 24-29, pp.311-318.
[79].Varady, T., Martin, R.R., Cox, J.:Reverse Engineering of Geometric Models-An Introduction, Computer-Aided Design,29 (4),1997, pp 255-269.
[80].Vosselman, G. and Dijkman, S.,2001.3D building model reconstruction from point clouds and ground planes, International Archieves of Photogrammetry, Remote Sensing and Spatial Information Sciences, vol.ⅩⅩⅩⅣ, part3/W4, Annapolis, pp.37-43.
[81].Vosselman G. Fusion of Laser Scanning Data, Maps, and Aerial Photographs for Building Reconstruction [C].2002 IEEE International Geoscience and Remote Sensing Symposium and the 24th Canadian Symposium on Remote Sensing, Toronto,2002.
[82].Vosselman, G. (2001).3D Building Model Reconstruction from Point Clouds and Ground Plans. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences 34(3/W4):37-43.
[83].Vosselman, G., Gorte,B.GH., Sithole, G.and Rabbani, T.2005. Recognising structure in laser scanner point clouds. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Science. ISPRS WG Ⅲ/4, Ⅴ/3 Workshop"Laser scanning 2005", Enschede, The Netherlands, September 12-14,2005.
[84].Vosselman, M.G., Gorte, B.G.H., Sithole, G. and Rabbani, T.,2004. Recognising structure in laser scanning point clouds, International Archieves of Photogrammetry, Remote Sensing and Spatial Information Science, vol. ⅩⅩⅩⅥ, part 8/W2, Freiburg, Germany, pp.33-38."
[85].W L Wolf.G J Zissis.The Infrared Handbook,Chapt.23 1978.
[86].Wallis,K.F.(1976)Seasonal adjustment and relations between variables Journal of the American Statistical Association,69(345)pp.18-31.
[87].Woo H, Kang E, Wang S, et al. A new segmentation method for point cloud data[J]. International Journal of Machine Tools& Manufacture,2002,42:167-178.
[88].Wunderlich,T.A.Operational and economic prospects of terrestrial laser scanning[A]. In: Proceedings of Optical 3D Measurement Techniques V[C],Vienna,2001:18-25.
[89].Yuriy Reshetyuk,2006. Investiggation and calibration of pusled time of flight terrestrial laser scanners. Ph.D thesis, Royal Institute of Technology (KTH), Division of Geodesy, Stockholm, Sweden.
[90]. Zhang, Z.,1994. Iterative point matching for registration of free-form curves and surfaces. International Journal of Computer Vision 13(2),119-152.
[91].Zimmer Y, Tepper R, Akselrod S.1997. An improved method to compute the convex hull of a shape in a binary image. PR,30:397-402.
[92].蔡润彬.地面激光扫描数据后处理若干关键技术研究,同济大学博士学位论文,2008.
[93].陈付幸,王润生.基于预检验的快速随机抽样一致性算,软件学报,2005,16(8)：1431-1437.
[94].陈远,陈震.基于CCD图像的点云区域分割方法,南昌航空工业学院院报,2007年01期.
[95].邓非.LIDAR数据与数字影像的配准和地物提取研究,武汉大学博士学位论文,2006.
[96].邓非.利用激光扫描和数码相机进行古建筑三维重建研究,测绘科学,2007年第2期.
[97].方惠兰,王国瑾.三角网格曲面上离散曲率估算方法的比较与分析,计算机辅助设计与图形学学报,2005,17(11)：2500-2507.
[98].冯文灏.近景摄影测量,武汉大学出版社,2002.
[99].张国印.高等数学,南京大学出版社,2009.
[100].官云兰,程效军,施贵刚.一种稳健的点云数据平面拟合方法[J],同济大学学报(自然科学版),2008-36(7)：981-984.
[101].管海燕.LIDAR与影像结合的地物分类及简单建筑物重建研究,武汉大学博士学位论文,2009.
[102].韩丽,高小山,楚秉智.离散曲率约束的三角网络模型拓扑分割算法,计算机辅助设计与图形学学报,第21卷第6期,2009年6月.
[103].何文峰.大型场景三维重建中的深度图像配准[D].北京大学硕士学位论文,2004.
[104].胡少兴,查红彬,张爱武.大型古文物真三维数字化方法[J],系统仿真学报,2006,18(4)：951-954,963.
[105].胡少兴,查红彬.利用轮廓特征的多视点几何数据配准[J],系统仿真学报,2007,19(6)：1307-1311.
[106].胡寅.三维扫描仪与逆向工程关键技术研究[D],华中科技大学博士学位论文,2005.
[107].柯映林,范树迁.基于点云的边界特征直接提取技术,机械工程学报,2004,40(9)：116-120.
[108].孔祥元,郭际明,刘宗泉.大地测量学基础,武汉大学出版社,2001.
[109].李海滨.基于影像序列的三维重建的研究与实践[D],解放军信息工程大学硕士学位论文,2007.
[110].李治江.彩色影像色调重建的理论与实际,武汉大学博士论文,2005.
[111].林明华.机载激光雷达点云数据处理理论与应用研究,武汉大学博士学位论文,2008.
[112].刘长征,丁辰,郭静珺.基于三维扫描数据的面分割空间三维重建方法[J],测绘技术装备,2004,6(4)：26-29.
[113].刘春.基于自适应紧支撑径向基函数的点云三维建模,地理与地理信息科学,2009年第25卷第1期.
[114].刘雪梅,庄晋林,张树生,李炳胜.利用自适应模糊椭球聚类实现点云分区,计算机工程与应用,2007年15期.
[115].刘雪梅,张树生,洪歧,黄绍林.逆向工程中基于模糊聚类的点云数据分区,机械科学与技术,2007年第26卷第4期.
[116].鲁铁定,周世健,张立亭,管云兰.基于整体最小二乘的地面激光扫描标靶球定位方法,大地测量与地球动力学报,2009年第4期.
[117].路银北,张蕾,普杰信,杜鹏.基于曲率的点云数据配准算法,计算机应用,2007(11).
[118].罗良峰.离散三角网格上的法向量和曲率估计,大连理工大学硕士学位论文,2007.
[119].罗先波.三维扫描系统中的数据配准技术[J],清华大学学报(自然科学版),2004,44(8)：1104-1106.
[120].潘俊.立体正射影像无缝镶嵌技术研究[D],武汉大学博士学位论文,2005.
[121].钱归平,童若锋,彭文,董金祥.基于散乱点云内部特征的网格重建,浙江大学学报(工学版),2008年第5期.
[122].沈立.反求工程中数据对正和多视角拼合技术的研究和实践[D],上海交通大学硕士学位论文,2001.
[123].施贵刚.地面三维激光扫描数据处理技术及作业方法的研究,同济大学博士学位论文,2009.
[124].史桂蓉,邢渊,张永清.用神经网络进行散乱点的区域分割,上海交通大学学报,2001,35(7).
[125].舒宁.激光成像[M].武汉大学出版社,2005.
[126].孙殿柱,范志先,李延瑞.散乱数据点云边界特征自动提取算法,华中科技大学学报(自然科学版),2008,36(8)：82-84.
[127].孙即祥.现代模式识别[M].长沙：国防科技大学出版社,2002：13-45.
[128].孙世为,王耕耘,李志刚.逆向工程中多试点点云的拼合方法[J],计算机辅助工程,2002,3(1)：8-12.
[129].唐亮.城市航空影像关键地物提取技术研究,西安电子科技大学博士学位论文,2004.
[130].王晏民,郭明,王国利,赵有山,李玉敏,胡春梅.利用激光雷达技术制作故建筑正射影像图,北京建筑工程学院学报,2006年第4期.
[131].吴敏,周来水等.测量点云数据的多视点拼合技术研究[J].南京航空航天大学学报,2003,25(5)：552-557.
[132].向日华,王润生.一种基于高斯混合模型的距离图像分割算法[J].软件学报,2003年07期,66-73.
[133].谢洪.地面三维激光雷达点云数据处理技术与应用研究,武汉大学硕士学位论文,2009.
[134].徐帆.无组织多视图图像的自动化三维场景重建,华中科技大学博士学位论文,2007.
[135].徐艳芳,黄敏,刘浩学,武兵.基于色调区域分割的扫描仪颜色转换,信息记录材料,2007年第1期.
[136].闫利,张毅.基于法向量模糊聚类的道路面点云数据滤波[J],武汉大学学报(信息科学版),2007.12.
[137].颜庆津.数值分析,北京航空航天大学出版社,2005.
[138].尤红建.激光三维遥感数据处理及建筑物重建,测绘出版社,2006.
[139].曾齐红,毛建华,李先华,刘学锋.武汉大学学报(信息科学版),2008年第1期,Vol.33 No.1.
[140].曾齐红.机载激光雷达点云数据处理与建筑物三维重建,上海大学博士学位论文,2009.
[141].翟瑞芳.激光点云和数字影像结合的小型文物重建研究,武汉大学博士学位论文,2006.
[142].张爱武,孙卫东,葛城辉,李风亭.室外大型场景多机位三维数据全局快速配准,2004(6),高技术通讯,pp6-10.
[143].张剑清,翟瑞芳,郑顺义.激光扫描多三维视图的全自动无缝镶嵌,武汉大学学报(信息科学版),2007年第2期.
[144].张凯.三维激光扫描数据的空间配准研究,南京师范大学硕士学位论文,2008.
[145].张力,张祖勋,张剑清.Wallis滤波在影像匹配中的应用,武汉测绘科技大学学报,Vol.24No.1,1999(3).
[146].张瑞菊.一种新的针对中国古建筑室内三维激光扫描数据的配准方法[J],激光杂志,2006,27(6)：63-65.
[147].张瑞乾.逆向工程中对测量数据进行重定位的研究[J],烟台大学学报(自然科学与工程版),2004,17(1)：55-58,63.
[148].张小红.机载激光雷达测量技术理论与方法,武汉大学出版社,2005,p.153.
[149].张小红,耿江辉.用不变矩从机载激光扫描测高点云数据中重建规则房屋[J],武汉大学学报(信息科学版),2006,31(2)：168-171.
[150].张小红.机载激光雷达测量技术理论与方法,武汉大学出版社,2007.
[151].张义宽,张晓鹏,籍万新.三维点云拓扑特征的提取技术及应用,2007年全国模式识别学术会议.
[152].张毅.地面三维激光扫描点云数据处理方法研究,武汉大学博士学位论文,2008.
[153].张祖勋,张剑清.数字摄影测量学,武汉大学出版社,2005.
[154].章毓晋.图像工程,清华大学出版社,2005.
[155].郑德华,岳东杰,岳建平.基于几何特征约束的建筑物点云配准算法,测绘学报,2008年第4期.
[156].张力宁,刘元朋,张定华.利用模糊神经网络实现逆向工程中的区域分割,计算机工程与应用,2004年第40卷第31期.
[2]. A Ruiz, W Kornus, J Talaya, JL Colomer. TERRAIN MODELING IN AN EXTREMELY STEEP MOUNTAIN:A COMBINATION. OF AIRBORNE AND TERRESTRIAL LIDAR-Proceedings of XX congress of International Society,2006.
[3]. Abdelhafiz, A., Riedel, B. and Niemeier, W.,2005. Towards a 3D True Colored Space by the Fusion of Laser Scanner Point Cloud and Digital Photos. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol.XXXVI, part5/W17, Venice, Italy.
[4]. Abmayr T., Hartl F., Reinkoster M., Frohlich C. Terrestrial laser scanning-applications in cultural heritage conservation and civilengineering. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences,Volume XXXVI, Part 5/W17, ISSN 1682-1777 (2005).
[5]. Akca, D.,2004. Least squares 3D curve matching. Internal Technical Report at IGP-ETH, Zurich, March,27 pages.
[6]. Akca, D.,2007. Least squares matching of 3D surfaces.4th Symposium of Turkish Society for Photogrammetry and Remote Sensing, Istanbul, Turkey, June 5-7, (only on CD-ROM).
[7]. Akca, D. Least Squares 3D Surface Matching, Ph.D dissertation, Swiss Federal Institute of Technology Zurich,2007.
[8]. Alan Watt.3D Computer Graphics, Third Edition,2005.
[9]. Ameri, B. (2000). AUTOMATIC 3D BUILDING RECONSTRUCTION USING PLANE-ROOF STRUCTURES. ASPRS, Washington DC.
[10].Arun KS, Huang TS, Blostein SD. Least square fitting of two 3D point sets. IEEE Trans Pattern Anal Mach Intell 1987;9:698-700.
[11].Balis, V, Karamistos, S., Kotsis, I., Liapakis, C. And Simpas, N.,2004.3D Laser Scanning: Integration of Point Cloud and CCD Camera Video Data for the Production of High Resolution and Precision RGB Textured Models:Archaeological Monuments Surveying Application in Ancient India. In Proceedings of FIG Working Week, Athens, Greece, May 22-27.
[12].Besl,P.J., and Mckay, N.D., A method for registration of 3D shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence 14(2),239-256,1992.
[13].Boissonnat J.D. Geometric structures for three-dimensional shape representation[J]. ACM Trans Graphics,1984, V3(4):266-286.
[14].Bretar, F. and Roux, M.,2005. Hybrid image segmentation using LIDAR 3D planar primitives. International Archieves of Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XXXVI, part3/W19, Enschede, the Netherlands, pp,232.
[15].Briese C.2006:Structure line modelling based on terrestrial laserscanner data, ISPRS Symposium, Dresden, Commission V-Image Engineering and Vision Metrology.
[16].Brinkhoff, T.,2004. Spatial access methods for organizing laser scanner data. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences 35(B4),98-102.
[17].Bruno Caprile, Vincent Torre:Using vanishing points for camera calibration. International Journal of Computer Vision 4(2):127-139 (1990).
[18].Callieri, M. and Cignoni, P. and Corsini, M. and Scopigno, R. Masked photo blending: Mapping dense photographic data set on high-resolution sampled 3D models, Computers\& Graphics, Volume32,No.4,p464-473,2008.
[19].Chen Y, Medioni G Object modeling by registration of multiple range images[A]. Proc IEEE Int'l conf on Robotics and Automation[C],1992,2724-2729.
[20].Cheng Qian Fengting Li Chenghui Ge. Feature extraction from range images in 3D modeling of urban scenes, Robotics, Intelligent Systems and Signal Processing,2003. Proceedings.2003 IEEE International Conference, Volume:2, On page(s):909-914 vol.2.
[21].Dan Sunday. Intersection of line, segment, and plane in 2D and 3D,2001, http://www.softsurfer.com.
[22].David A. Forsyth and Jean Ponce. Computer Vision, a modern approach. Prentice Hall. ISBN ISBN 0-13-085198-1(2003).
[23].Desbrun M.Meyer M.Schroder P Discrete differential-geometry operators for triangulated 2-manifolds 2002.
[24].Ed Catmull, A Subdivision Algorithm for Computer Display of Curved Surfaces, PhD Thesis, University of Utah,1974.
[25].Evvgeny Lomonosov, Dmitry Chetverikov. Pre-registration of arbitrarily oriented 3D surfaces using a genetic algorithm, Computer and Automation Research Institute Budapest, 2005, Kende u.13-17, H-1111.
[26].Feng, Q.H. and K.Roshoff.,2004. In-situ mapping and documentation of rock faces using a full-coverage 3D laser scanning technique. International Journal of Rock Mechanics and Mining Sciences,41(Supplement 1):p.139-144.
[27].Filin,S.,2002. Surface clustering from airborne laser scanning data. International Archieves of Photogrammetry, Remote Sensing and Spatial Information Sciences, vol.XXXIV, part3A/B, Graz, Austria, pp.119-124.
[28].Forkert, G., Kerschner, M., Prinz, R., and Rottensteiner, F.,1995. Reconstruction of free-formed spatial curves from digital images. International Archives of Photogrammetry and Remote Sensing 30(5/W1),163-168.
[29].Gielsdorf,F.,A.Rietdorf, L.Gruending:A concept for the calibration of terrestrial laser scanners. In:Proc.of FIG Working Week, Athens,Greece,2004.
[30].Goldman, Ronald N. Intersection of three planes. In Andrew Glassner, editor, Graphics Gems, Academic Press, San Diego, page 305,1990.
[31].Gorte, B.G.H.,2002. Segmentation of tin-structured surface models, Joint Conference on Geo-spatial theory, Processing and Applications, Ottawa, Canada,8-12 July 2002.
[32].Gruen, A.,1985. Adaptive least squares correlation:a powerful image matching technique. South Africa Journal of Photogrammetry, Remote Sensing and Cartography 14(3),175-187.
[33].Gruen, A., and Li, H.,1996. Linear feature extraction with LSB-Snakes from multiple images. International Archives of Photogrammetry and Remote Sensing 31(3B),266-272.
[34].Hom B K P.Closed-form solution of absolute orientation using unit quaternions[J]. Journal of the Optical Society of America,1987,4(4):629-642.
[35].Hoover A.Gillian J B.Jiang Xiao-yi. An experimental comparison of range image segmentation algorithms, IEEE Transactions on Pattern Analysis and Machine Intelligence archive,1996(7).
[36].Reference from http://en.wikipedia.org/wiki/Kd-tree.
[37].I.Jazayeri, C.S.Fraser. Interest Operators in Close-Range Object Reconstruction,2008, http://www.isprs.org/proceedings/XXXVII/congress/5_pdf/12.pdf.
[38].Ioannidis, Ch., Demir, N., Soile, S., Tsakiri, M.. Combination of Laser Scanner Data and Simple Photogrammetric Procedures for Surface Reconstruction of Monuments, The CIPA International Archives forDocumentation of Cultural Heritage Volume XX-2005, pp.372-377 (2005).
[39]. J Bohm, N Haala. Efficient Integration of Aerial and Terrestrial Laser Data for Virtual City Modeling Using LASERMAPS-Proceedings of the ISPRS Workshop, Laser scanning,2005-elib.uni-stuttgart.de.
[40].J. Bohm, N. Haala. Efficient integration of aerial and terrestrial laser data for virtual city modeling using lasermaps in:IAPRS VOLUME XXXVI, part3/w19,2005.
[41].Jiang,X.Y.[Xiao-Yi],Bunke,H.,Meier,U.,High-level feature based range image segmentation, IVC(18), No.10, July 2000, pp.817-822.
[42].Jokinen, O.,1998. Area-based matching for simultaneous registration of multiple 3-D profile maps. Computer Vision and Image Understanding 71(3),431-447.
[43].Kazhdan, M. and Bolitho, M. and Hoppe, H. Poisson surface reconstruction, Proceedings of the fourth Eurographics symposium on Geometry processing,p.70,2006, Eurographics Association.
[44].Laura Chasmer, Chris Hopkinson, and Paul Treitz. Investigating laser pulse penetration through a conifer canopy by integrating airborne and terrestrial lidar, Canadian Journal of Remote Sensing, volume32, number2, Apirl 2006.
[45].Lee,I.,Schenk, T.,2001.3D perceptual organization of laser altimetry data. International Archives of Photogrammetry, Remote Sensing and Spatial Information Science 34(Part 3/W4),57-65.
[46].Lindenbergh, R., N. Pfeifer:A statistical deformation analysis of two epochs of terrestrial laser data of a lock. In Proc. of Optical 3D Measurement Techniques, Vol II, pp 61-70, Vienna, Austria,2005.
[47].Lionel Penard.3D Building Fa?ade Reconstruction under Mesh Form from Multiple Wide Angle Views, www.isprs.org/commission5/3darch05/pdf/17.pdf.
[48].Liu, Land Yu, Gand Wolberg, Gand Zokai,S. Multiview Geometry for Texture Mapping 2D Image s onto 3D Range Data,2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Volum2,2006.
[49].M J Milroy. Segmentation of a wrap-around model using an active contour, Computer-Aided Design, Volume 29, Number 4, April 1997, pp.299-320(22)
[50].M.A. Fischler and R. C. Bolles (June 1981).""Random Sample Consensus:A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography"". Comm. of the ACM 24:381--395. doi:10.1145/358669.358692.
[51].O.D.Faugeras and M.Hebert. The representation, recognition and locating of 3D objects, Int.J.Robotics Res.5,1986,27-52.
[52].Overby,J., Bodum, L., Kjems, E. and Ilsoe, P.M. Automatic 3D Building Reconstruction from Airborne Laser Scanning and Cadastral Data Using Hough Transform. International Archieves of Photogrammetry, Remote Sensing and Spatial Information Science, vol.XXXV,
part B3, Istanbul,Turkey,2004.
[53].Penard, L., Paparoditis, N., Pierrot-Deseilligny, M.,2005.3D building fa?ade reconstruction under mesh form from multiple wide angle views. In:Proceedings of International Workshop "3D-ARCH 2005", Mestre-Venice, Italy, IAPRS, vol. ⅩⅩⅩⅥ, part 5/W17, http://www.commission5.isprs.org/3darch05/pdf/17.pdf.
[54].Philip J. Schneider, David H.Eberly.'Geometric Tools for Computer Graphics', Elsevier Science(USA),2005.
[55].Piegl, Les, and Wayne Tiller.1995, The NURBS BOOK. Springer-Verlag, Berlin.
[56].Pottmann, H., Leopoldseder, S., and Hofer, M.,2004. Registration without ICP. Computer Vision and Image Understanding 95(1),54-71.
[57].Pu,S., Vosselman,G..Automatic extraction of building features from terrestrial laser scanning, International Archieves of Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol.36, part 5, Dresden, Germany, September 25-27,2006.
[58].Punya Prasad Sapkota. Segmentation of Coloured Point Cloud Data, ITC MSC Thesis, Enschede,the Netherlands,2008.
[59].Q. Huang and B. Dom, ""Quantitative methods of evaluating image segmentation,"" ICIP, vol.3, p.3053,1995.
[60].Qingming Zhan, Yubin Liang, Yinghui Xiao. Color-Based Segmentation of Point Clouds, http://www.isprs.org/proceedings/ⅩⅩⅩⅧ/3-W8/papers/p65.pdf.
[61].Rabbani,T., van den Heuvel, F.A and Vosselman, M.G.,2006. Segmentation of point clouds using smoothness constraints. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. ⅩⅩⅩⅥ, part 5, Dresden, Germany, pp.248-253.
[62].Remondino,F.2006, Image-based modelling for object and human reconstruction.PHD dissertation, Institute of Geodesy and Photogrammetry, ETH Zurich, Mitteilungen No.91,174pp.
[63].Richard Hartley and Andrew Zisserman. Multiple View Geometry in Computer Vision,2nd edition, Cambridge University Press(2003).
[64].Rogers, D.F., and Adams, J.A.,1976. Mathematical elements for computer graphics. McGraw-Hill Book Company, London, pp.116-155.
[65].Roggero,M.,2002. Object segmentation with region growing and principle component analysis. International Archives of Photogrammetry, Remote Sensing and Spatial Information Science 34(Part 3A),289-294.
[66].S. Li and D. Zhao, Gradient-based polyhedral segmentation for range images, Pattern Recognition Letters 24 (2003) (12), pp.2069-2077.
[67].S.Ferrari, G.Ferrigno, V.Piuri, N.A. Borghese.(2007) Reducing and Filtering Point Clouds with Enhanced Vector Quantization. IEEE Trans. On Neural Networks. Volume 18, Issue 1, Jan.2007 Pages:161-177.
[68].Schnabel, R., Wahl, R. and Klein, R.,2007b. Shape Detection in Point Clouds. Computer Graphics Technical report, No. CG-2006-2, University of Bonn, Germany.
[69].Shahar Barnea, Sagi Filin. Keypoint based autonomous registration of terrestrial laser point-clouds. ISPRS Journal of Photogrammetry& Remote Sensing 63,19-35 (2008).
[70].Sithole, G.,2005. Segmentation and classification of airborne laser scanning data. Ph.D Thesis, Technical university of Delft.
[71].Skarbek, W., A. Koschan,1994. Color Image Segmentation. Report, Technical University, Berlin.
[72].Slob, S. and Hack, R.:3-D Terrestrial Laser Scanning as a New Field Measurement and Monitoring Technique, in:Engineering Geology for Infrastructure Planning in Europe, A European Perspective, edited by:Hack, R., Azzam, R., and Charlier, R., Lecture Notes in Earth Sciences, Springer, Berlin/Heidelberg,104,179-190,2004.
[73].Talaya,J.,Bosch,E.,Serra,A.,Alamus,R.,Bosch,E.,Kornus,W.,2004b."Integration of a Terrestrial Laser Scanner with GPS/IMU Orientation Sensors.", International Archives of Photogrammetry and Remote Sensing,2004, Istambul, Turkey.
[74].Tao, V.C..3D Data Acquisition and Object.Reconstruction for AEC and CAD. In:S. Zlatanova and D. Prosperi (Editors), Large-scale 3D Data.Integration. CRCpress, Boca Raton, Florida, USA,pp.245 (2005).
[75].Tarsha-Kurdi, F., Landes, T. and Grussenmeyer, P.,2007. Hough-Transform and Extended RANSAC Algorithms for Automatic Detection of 3D Building Roof Planes from LIDAR Data. International Archieves of Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. ⅩⅩⅩⅥ, part3/W52, Espo, Finland, pp.407-412.
[76].TATIANA S.EVGENY M.OCTAVIAN S.A comparison of Gaussian and mean curvatures estimation methods on triangular meshes,2003.
[77].T6vari, D.,2006. Segmentation based classification of airborne laser scanner data. Ph.D Thesis, university Karlsruhe, Germany.
[78].Turk, G., and Levoy, M.,1994. Zippered polygon meshes from range images. Proc. of ACM SIGGRAPH'94, Orlando(Florida), July 24-29, pp.311-318.
[79].Varady, T., Martin, R.R., Cox, J.:Reverse Engineering of Geometric Models-An Introduction, Computer-Aided Design,29 (4),1997, pp 255-269.
[80].Vosselman, G. and Dijkman, S.,2001.3D building model reconstruction from point clouds and ground planes, International Archieves of Photogrammetry, Remote Sensing and Spatial Information Sciences, vol.ⅩⅩⅩⅣ, part3/W4, Annapolis, pp.37-43.
[81].Vosselman G. Fusion of Laser Scanning Data, Maps, and Aerial Photographs for Building Reconstruction [C].2002 IEEE International Geoscience and Remote Sensing Symposium and the 24th Canadian Symposium on Remote Sensing, Toronto,2002.
[82].Vosselman, G. (2001).3D Building Model Reconstruction from Point Clouds and Ground Plans. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences 34(3/W4):37-43.
[83].Vosselman, G., Gorte,B.GH., Sithole, G.and Rabbani, T.2005. Recognising structure in laser scanner point clouds. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Science. ISPRS WG Ⅲ/4, Ⅴ/3 Workshop"Laser scanning 2005", Enschede, The Netherlands, September 12-14,2005.
[84].Vosselman, M.G., Gorte, B.G.H., Sithole, G. and Rabbani, T.,2004. Recognising structure in laser scanning point clouds, International Archieves of Photogrammetry, Remote Sensing and Spatial Information Science, vol. ⅩⅩⅩⅥ, part 8/W2, Freiburg, Germany, pp.33-38."
[85].W L Wolf.G J Zissis.The Infrared Handbook,Chapt.23 1978.
[86].Wallis,K.F.(1976)Seasonal adjustment and relations between variables Journal of the American Statistical Association,69(345)pp.18-31.
[87].Woo H, Kang E, Wang S, et al. A new segmentation method for point cloud data[J]. International Journal of Machine Tools& Manufacture,2002,42:167-178.
[88].Wunderlich,T.A.Operational and economic prospects of terrestrial laser scanning[A]. In: Proceedings of Optical 3D Measurement Techniques V[C],Vienna,2001:18-25.
[89].Yuriy Reshetyuk,2006. Investiggation and calibration of pusled time of flight terrestrial laser scanners. Ph.D thesis, Royal Institute of Technology (KTH), Division of Geodesy, Stockholm, Sweden.
[90]. Zhang, Z.,1994. Iterative point matching for registration of free-form curves and surfaces. International Journal of Computer Vision 13(2),119-152.
[91].Zimmer Y, Tepper R, Akselrod S.1997. An improved method to compute the convex hull of a shape in a binary image. PR,30:397-402.
[92].蔡润彬.地面激光扫描数据后处理若干关键技术研究,同济大学博士学位论文,2008.
[93].陈付幸,王润生.基于预检验的快速随机抽样一致性算,软件学报,2005,16(8)：1431-1437.
[94].陈远,陈震.基于CCD图像的点云区域分割方法,南昌航空工业学院院报,2007年01期.
[95].邓非.LIDAR数据与数字影像的配准和地物提取研究,武汉大学博士学位论文,2006.
[96].邓非.利用激光扫描和数码相机进行古建筑三维重建研究,测绘科学,2007年第2期.
[97].方惠兰,王国瑾.三角网格曲面上离散曲率估算方法的比较与分析,计算机辅助设计与图形学学报,2005,17(11)：2500-2507.
[98].冯文灏.近景摄影测量,武汉大学出版社,2002.
[99].张国印.高等数学,南京大学出版社,2009.
[100].官云兰,程效军,施贵刚.一种稳健的点云数据平面拟合方法[J],同济大学学报(自然科学版),2008-36(7)：981-984.
[101].管海燕.LIDAR与影像结合的地物分类及简单建筑物重建研究,武汉大学博士学位论文,2009.
[102].韩丽,高小山,楚秉智.离散曲率约束的三角网络模型拓扑分割算法,计算机辅助设计与图形学学报,第21卷第6期,2009年6月.
[103].何文峰.大型场景三维重建中的深度图像配准[D].北京大学硕士学位论文,2004.
[104].胡少兴,查红彬,张爱武.大型古文物真三维数字化方法[J],系统仿真学报,2006,18(4)：951-954,963.
[105].胡少兴,查红彬.利用轮廓特征的多视点几何数据配准[J],系统仿真学报,2007,19(6)：1307-1311.
[106].胡寅.三维扫描仪与逆向工程关键技术研究[D],华中科技大学博士学位论文,2005.
[107].柯映林,范树迁.基于点云的边界特征直接提取技术,机械工程学报,2004,40(9)：116-120.
[108].孔祥元,郭际明,刘宗泉.大地测量学基础,武汉大学出版社,2001.
[109].李海滨.基于影像序列的三维重建的研究与实践[D],解放军信息工程大学硕士学位论文,2007.
[110].李治江.彩色影像色调重建的理论与实际,武汉大学博士论文,2005.
[111].林明华.机载激光雷达点云数据处理理论与应用研究,武汉大学博士学位论文,2008.
[112].刘长征,丁辰,郭静珺.基于三维扫描数据的面分割空间三维重建方法[J],测绘技术装备,2004,6(4)：26-29.
[113].刘春.基于自适应紧支撑径向基函数的点云三维建模,地理与地理信息科学,2009年第25卷第1期.
[114].刘雪梅,庄晋林,张树生,李炳胜.利用自适应模糊椭球聚类实现点云分区,计算机工程与应用,2007年15期.
[115].刘雪梅,张树生,洪歧,黄绍林.逆向工程中基于模糊聚类的点云数据分区,机械科学与技术,2007年第26卷第4期.
[116].鲁铁定,周世健,张立亭,管云兰.基于整体最小二乘的地面激光扫描标靶球定位方法,大地测量与地球动力学报,2009年第4期.
[117].路银北,张蕾,普杰信,杜鹏.基于曲率的点云数据配准算法,计算机应用,2007(11).
[118].罗良峰.离散三角网格上的法向量和曲率估计,大连理工大学硕士学位论文,2007.
[119].罗先波.三维扫描系统中的数据配准技术[J],清华大学学报(自然科学版),2004,44(8)：1104-1106.
[120].潘俊.立体正射影像无缝镶嵌技术研究[D],武汉大学博士学位论文,2005.
[121].钱归平,童若锋,彭文,董金祥.基于散乱点云内部特征的网格重建,浙江大学学报(工学版),2008年第5期.
[122].沈立.反求工程中数据对正和多视角拼合技术的研究和实践[D],上海交通大学硕士学位论文,2001.
[123].施贵刚.地面三维激光扫描数据处理技术及作业方法的研究,同济大学博士学位论文,2009.
[124].史桂蓉,邢渊,张永清.用神经网络进行散乱点的区域分割,上海交通大学学报,2001,35(7).
[125].舒宁.激光成像[M].武汉大学出版社,2005.
[126].孙殿柱,范志先,李延瑞.散乱数据点云边界特征自动提取算法,华中科技大学学报(自然科学版),2008,36(8)：82-84.
[127].孙即祥.现代模式识别[M].长沙：国防科技大学出版社,2002：13-45.
[128].孙世为,王耕耘,李志刚.逆向工程中多试点点云的拼合方法[J],计算机辅助工程,2002,3(1)：8-12.
[129].唐亮.城市航空影像关键地物提取技术研究,西安电子科技大学博士学位论文,2004.
[130].王晏民,郭明,王国利,赵有山,李玉敏,胡春梅.利用激光雷达技术制作故建筑正射影像图,北京建筑工程学院学报,2006年第4期.
[131].吴敏,周来水等.测量点云数据的多视点拼合技术研究[J].南京航空航天大学学报,2003,25(5)：552-557.
[132].向日华,王润生.一种基于高斯混合模型的距离图像分割算法[J].软件学报,2003年07期,66-73.
[133].谢洪.地面三维激光雷达点云数据处理技术与应用研究,武汉大学硕士学位论文,2009.
[134].徐帆.无组织多视图图像的自动化三维场景重建,华中科技大学博士学位论文,2007.
[135].徐艳芳,黄敏,刘浩学,武兵.基于色调区域分割的扫描仪颜色转换,信息记录材料,2007年第1期.
[136].闫利,张毅.基于法向量模糊聚类的道路面点云数据滤波[J],武汉大学学报(信息科学版),2007.12.
[137].颜庆津.数值分析,北京航空航天大学出版社,2005.
[138].尤红建.激光三维遥感数据处理及建筑物重建,测绘出版社,2006.
[139].曾齐红,毛建华,李先华,刘学锋.武汉大学学报(信息科学版),2008年第1期,Vol.33 No.1.
[140].曾齐红.机载激光雷达点云数据处理与建筑物三维重建,上海大学博士学位论文,2009.
[141].翟瑞芳.激光点云和数字影像结合的小型文物重建研究,武汉大学博士学位论文,2006.
[142].张爱武,孙卫东,葛城辉,李风亭.室外大型场景多机位三维数据全局快速配准,2004(6),高技术通讯,pp6-10.
[143].张剑清,翟瑞芳,郑顺义.激光扫描多三维视图的全自动无缝镶嵌,武汉大学学报(信息科学版),2007年第2期.
[144].张凯.三维激光扫描数据的空间配准研究,南京师范大学硕士学位论文,2008.
[145].张力,张祖勋,张剑清.Wallis滤波在影像匹配中的应用,武汉测绘科技大学学报,Vol.24No.1,1999(3).
[146].张瑞菊.一种新的针对中国古建筑室内三维激光扫描数据的配准方法[J],激光杂志,2006,27(6)：63-65.
[147].张瑞乾.逆向工程中对测量数据进行重定位的研究[J],烟台大学学报(自然科学与工程版),2004,17(1)：55-58,63.
[148].张小红.机载激光雷达测量技术理论与方法,武汉大学出版社,2005,p.153.
[149].张小红,耿江辉.用不变矩从机载激光扫描测高点云数据中重建规则房屋[J],武汉大学学报(信息科学版),2006,31(2)：168-171.
[150].张小红.机载激光雷达测量技术理论与方法,武汉大学出版社,2007.
[151].张义宽,张晓鹏,籍万新.三维点云拓扑特征的提取技术及应用,2007年全国模式识别学术会议.
[152].张毅.地面三维激光扫描点云数据处理方法研究,武汉大学博士学位论文,2008.
[153].张祖勋,张剑清.数字摄影测量学,武汉大学出版社,2005.
[154].章毓晋.图像工程,清华大学出版社,2005.
[155].郑德华,岳东杰,岳建平.基于几何特征约束的建筑物点云配准算法,测绘学报,2008年第4期.
[156].张力宁,刘元朋,张定华.利用模糊神经网络实现逆向工程中的区域分割,计算机工程与应用,2004年第40卷第31期.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |