一种三维重建技术中基于点云粗配准方法
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摘要
伴随着计算机技术应用与三维扫描应用的发展,点云三维重建技术被普遍的应用到计算机辅助设计、虚拟现实、测量学、医学等各种领域.选取了斯坦福大学提供的点云数据作为研究对象,提出了从点云配准,点云降采样,点云滤波到重建三角面的实现方案.通过粗配准算法处理点云,使用精配准ICP算法;经过配准得到的点云降采样;采用双边滤波平滑点云;重建点云模型.通过对比斯坦福大学提供的重建后的模型,结果表明,本文的方法在点云三维重建方面有较好的表现.
With the development of computer technology applications and 3D scanning applications,point cloud 3D reconstruction technology has been widely applied to computer-aided design,virtual reality,surveying,medicine and other fields. The point cloud data provided by Stanford University was selected as the research object,and the implementation scheme from point cloud registration,point cloud down sampling,point cloud filtering to reconstruction triangle surface was proposed. The point cloud was processed by the coarse registration algorithm,and the fine-aligned ICP algorithm was used; the point cloud down-sampling was obtained by registration; the point cloud was smoothed by bilateral filtering; the point cloud model was reconstructed by triangulation. By comparing the reconstructed models provided by Stanford University,the results showed that the proposed method had a good performance in point cloud 3D reconstruction.
With the development of computer technology applications and 3D scanning applications,point cloud 3D reconstruction technology has been widely applied to computer-aided design,virtual reality,surveying,medicine and other fields. The point cloud data provided by Stanford University was selected as the research object,and the implementation scheme from point cloud registration,point cloud down sampling,point cloud filtering to reconstruction triangle surface was proposed. The point cloud was processed by the coarse registration algorithm,and the fine-aligned ICP algorithm was used; the point cloud down-sampling was obtained by registration; the point cloud was smoothed by bilateral filtering; the point cloud model was reconstructed by triangulation. By comparing the reconstructed models provided by Stanford University,the results showed that the proposed method had a good performance in point cloud 3D reconstruction.
引文
[1]牛晓静.玉米植株三维点云处理及重构方法研究[D].杨凌:西北农林科技大学,2016.
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[2] WU J,JIN F X,WANG J. 3D modeling of buildings based on 3D laser scanner data[J]. Engineering of Surveying and Mapping,2007,16(5):57-60.
[3]赵矿军.基于RGB-D摄像机的室内三维彩色点云地图构建[J].哈尔滨商业大学学报:自然科学版,2018,34(1):66-74.
[4] BIBER P,STRASSER W. The normal distributions transform:a new approach to laser scan matching[C]//IEEE/RSJ International Conference on Intelligent Robots and Systems IEEE,2003,3:2743-2748.
[5] MAGNUSSON M,LILIENTHALS A,DUCKETT T. Scan registration for autonomous mining vehicles using 3D-NDT[M].[S. l.]:John Wiley and Sons Ltd. 2007.
[6] AIGER D,MITRA N J,COHEN-OR D. 4-points congruent sets for robust pairwise surface registration[J]. ACM Transactions on Graphics,2008,27(3):1-10.
[7] MELLADO N,AIGER D,MITRA N J. Super 4PCS Fast Global Pointcloud Registration via Smart Indexing[J]. Computer Graphics Forum,2015,33(5):205-215.
[8]韩宝昌.基于区域的自动点云配准算法[D].大连:大连理工大学,2014.
[9]李选富.散乱点云自动配准技术研究[D].哈尔滨:哈尔滨工业大学. 2010.
[10]张永恒.散乱点云数据配准方法研究[D].西安:长安大学,2015.
[11] HORN B K P. Closed form solution of absolute orientation using unit quaternions[J]. Journal of the Optical Society of America A,1987,4(4):629-642.
[12] RIZZO M L. Numerical Analysis For Statisticians[J]. Biometrics,2011,67(3):1180-1181.
[13] BESL P J,MCKAY N D. A method for registration of 3-D shapes[J]. IEEE Transaction on Pattern Analysis and Machine Intelligence,1992,14(2):239-256.
[14] FLEISHMAN S,DRORI I,COHENOR D. Bilateral mesh denoising[C]//ACM Siggraph. ACM,2003:950-953.
[15] AMENTA N. A new Voronoi-based surface reconstruction algorithm[C]//SIGGRAPH 1998 Conference Proceedings.1998:415-421.