Sieving Scattered 3D Point Clouds Using Clustering Analysis for 3D Surface Reconstruction
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    Abstract:

    During the course of 3d surface reconstruction, there are a large number of noises and isolated 3d points in raw 3d point clouds, which obtained from images. If we directly use these data to reconstruct surface, the algorithm will make surface sharply prominent and ineffective reconstruction. Because of above problems, a method that sieving 3d point clouds based on DBSCAN is presented in this paper, and then 3d surface is reconstructed using filtered 3d point clouds. Experiments show that good 3d surface reconstruction is obtained using this algorithm.

    Reference
    1 郑德华,沈云中,刘春.三维激光扫描仪及其测量误差影素分析.测绘工程,2005,14(2):32-34.
    2 胡小强. 虚拟现实技术与应用. 北京: 高等教育出,2004.165-170.
    3 Ester M. Kriegel H, Sander J, Xu X. A density-based algorithm for discovering clusters in large spatial databases with noise. Proc. 2nd Int. Conf. on Knowledge Discovery and Data Mining, Portland, 1996:226-235.
    4 Martinec D, Pajdla T, Kostkova J, Sara R.3D Reconstruction by Gluing Pair-wise Euclidean Reconstructions, or How to Achieve a Good Reconstruction from Bad Images, [2009-12-15].http://cmp.felk.cvut.cz/demos/Reconstruction/demo3DPVT06/
    5 Giaccari L. Surface Reconstruction from Scattered Points Cloud: MyCrust Robust. [2010-3-5]. http://www.Advancedmcode.org/surface-recostruction–from-scattered-points-cloud-mycrust-robust.html
    6 Martinec D. Robust Multiview Reconstruction. Research Reports of CMP, Czech Technical University in Prague, No.1, 2008. [2009-12-15]. ftp://cmp.felk.cvut.Cz/pub/cmp/articles/martinec/Martinec-thesis.pdf
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陈晓霞,陈孝威.三维重建中散乱点云的聚类筛选与网格重建.计算机系统应用,2011,20(4):141-144

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  • Received:September 03,2010
  • Revised:October 20,2010
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