基于加权约束的单体建筑物点云表面重建算法
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摘要
建筑物点云表面重建在高精度城市测绘、虚拟现实等领域有十分广泛的应用前景。由于建筑物的几何形态多变,重建算法普遍存在计算速率慢、拟合精度低和模型结构不完整的问题。为此,本文以单体建筑物为研究对象,提出基于加权约束的单体建筑物点云表面重建算法,在表面初始化过程中充分考虑数据对结构拟合的贡献。在此基础上,构建基于正则集的单体建筑物表面重建算法,实现建筑物拟合过程中的加权拟合误差、近邻结构平滑的同步优化。针对多类建筑物三维点云的实验结果表明,相比传统的建筑物重建策略,本文的加权约束方法可根据不同类型的点云数据设计自适应权重,并选择模型拟合中最优的权重函数,在高噪声、低精度点云数据下能得到更高精度的单体建筑物表面模型。
Building reconstruction based on 3D point cloud data has broad application prospects in fields such as high precision urban mapping and virtual reality. Due to the diverse geometry of buildings, there are widespread problems in traditional reconstruction algorithms, e.g., slow computation speed, low fitting precision, and incompleteness of building structures. Thus, with single-building as the research object, this paper proposed an algorithm based on weighted constraints for reconstructing point cloud surfaces. By fully considering each point's contribution to the fitting plane during the surface initialization process, the proposed algorithm, which is based on regular sets, simultaneously optimizes the error of adaptively weighted fitting and the smoothness of neighbor structures. The algorithm was applied to the 3D point clouds of various buildings. Results showed that,compared with conventional building reconstruction strategies, the weighted-constraints based algorithm of this study can design adaptive weights according to different types of point clouds, and can choose the optimal weight for model fitting. In cases where the point cloud data contain high noise and low accuracy, the proposed algorithm can help generate more accurate surface models for single-building.
Building reconstruction based on 3D point cloud data has broad application prospects in fields such as high precision urban mapping and virtual reality. Due to the diverse geometry of buildings, there are widespread problems in traditional reconstruction algorithms, e.g., slow computation speed, low fitting precision, and incompleteness of building structures. Thus, with single-building as the research object, this paper proposed an algorithm based on weighted constraints for reconstructing point cloud surfaces. By fully considering each point's contribution to the fitting plane during the surface initialization process, the proposed algorithm, which is based on regular sets, simultaneously optimizes the error of adaptively weighted fitting and the smoothness of neighbor structures. The algorithm was applied to the 3D point clouds of various buildings. Results showed that,compared with conventional building reconstruction strategies, the weighted-constraints based algorithm of this study can design adaptive weights according to different types of point clouds, and can choose the optimal weight for model fitting. In cases where the point cloud data contain high noise and low accuracy, the proposed algorithm can help generate more accurate surface models for single-building.
引文
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[4] Edelsbrunner H, Mucke E. Three dimension alpha shapes[J]. ACM Transactions on Graphics, 1994,13(1):43-72.
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[2] Labatut P, Pons J P, Keriven R. Robust and efficient surface reconstruction from range data[C]//Computer graphics forum. Oxford, UK:Blackwell Publishing Ltd, 2009,28(8):2275-2290.
[3] Amenta N, Bern M W, Kamvysselis M. A new voronoibased surface reconstruction algorithm[C]//Siggraph. 1998,98:415-421.
[4] Edelsbrunner H, Mucke E. Three dimension alpha shapes[J]. ACM Transactions on Graphics, 1994,13(1):43-72.
[5] Amenta N, Bern M, Kamvy. A new voronoi-based surface reconstruction algorithm[C]//Proceedings of SIGGRAPH’98, 1998:19-24.
[6] Brinkley J F. Knowledge-driven ultracsonic three-dimensional organ modeling[C]//IEEE Trans. Pat. Anal. Mach.Intell, 1985,7(4):431-441.
[7] Schmitt F, Barsky B A, Du W. An adaptive subdivision method for surface fitting from sampled data[J]. Computer Graphics, 1986,20(4):179-128.
[8]邵磊,董广军,于英,等.车载激光扫描数据中建筑物立面快速提取[J].地球信息科学学报,2018,20(4):462-470.[Shao L, Dong G J, Yu Y, et al. A fast method of building extraction from mobile Li DAR scanning data[J]. Journal of Geo-information Science, 2018,20(4):462-470.]
[9]刘凯斯,王彦兵,宫辉力,等.机载Li DAR点云数据的二面角滤波算法[J].地球信息科学学报,2018,20(4):414-421.[Liu K S, Wang Y B, Gong H L, et al. Dihedral angle filtering algorithm for airborne Li DAR point cloud data[J]. Journal of Geo-information Science, 2018,20(4):414-421.]
[10] Schnabel R, Wahl R, Klein R. Efficient RANSAC for point‐cloud shape detection[C]//Computer graphics forum. Oxford, UK:Blackwell Publishing Ltd, 2007,26(2):214-226.
[11] Ni K, Jin H, Dellaert F. Groupsac:Efficient consensus in the presence of groupings[C]//Computer Vision, 2009 IEEE12th International Conference on. IEEE, 2009:2193-2200.
[12] Chum O, Matas J. Matching with PROSAC-progressive sample consensus[C]//Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on. IEEE, 2005,1:220-226.
[13] Wong H S, Chin T J, Yu J, et al. Dynamic and hierarchical multi-structure geometric model fitting[C]//Computer Vision(ICCV), 2011 IEEE International Conference on.IEEE, 2011:1044-1051.
[14] Jenke P, Krückeberg B, Stra?er W. Surface reconstruction from fitted shape primitives[C]//Vision, Modeling&Visualization Conference, Konstanz, Germany, 2008:31-40.
[15] Chen J, Chen B. Architectural modeling from sparsely scanned range data[J]. International Journal of Computer Vision, 2008,78(2-3):223-236.
[16] Chauve A L, Labatut P, Pons J P. Robust piecewise-planar3D reconstruction and completion from large-scale unstructured point data[C]//Computer Vision and Pattern Recognition(CVPR), 2010 IEEE Conference on. IEEE,2010:1261-1268.
[17] Lafarge F, Alliez P. Surface reconstruction through point set structuring[C]//Computer Graphics Forum. Oxford,UK:Blackwell Publishing Ltd, 2013,32(2pt2):225-234.
[18] Gallup D, Frahm J M, Mordohai P, et al. Real-time planesweeping stereo with multiple sweeping directions[C]//2007 IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 2007:1-8.
[19] Straub J, Rosman G, Freifeld O, et al. A mixture of manhattan frames:Beyond the manhattan world[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2014:3770-3777.
[20] Arikan M, Schw?rzler M, Fl?ry S, et al. O-snap:Optimization-based snapping for modeling architecture[J]. ACM Transactions on Graphics(TOG), 2013,32(1):6.
[21] Hu P B, Yang B S, Dong Z, et al. Towards reconstructing3D buildings from ALS data based on gestalt laws. Remote Sensing, 2018,10(7):1127.
[22] Monszpart A, Mellado N, Brostow G J, et al. RAPter:Rebuilding man-made scenes with regular arrangements of planes[J]. ACM Transactions on Graphics, 2015,34(4):10301-10312.
[23] Mackiewicz A, Ratajczak W. Principal components analysis(PCA)[J]. Computers and Geosciences, 1993,19:303-342.
[24] Oesau S, Lafarge F, Alliez P. Indoor scene reconstruction using feature sensitive primitive extraction and graph-cut[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2014,90:68-82.
[25] Guerrero P, Kleiman Y, Ovsjanikov M, et al. PCPNet learning local shape properties from raw point clouds[C]//Computer Graphics Forum, 2018,37(2):75-85.