基于三维点云数据的表面重构算法在工业设计中的应用

Y. Haibo
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引用次数: 5

摘要

目前,快速重建大量点云数据的方法仍然很少,现有方法的时间复杂度和空间复杂度也很低。针对存储和传输的数据量,提出了一种基于自适应栅格化的三角网格重构方法。我们的方法改进了区域扩展:首先,采用各点无差的宏观估计方法,得到边长的三维网格,并将点云数据分离成网格单元;然后,选取基本单元中的数据点作为种子点,以三角形边长近似正邻域为约束,构造初始三角形网格;最后,通过逐层展开完成三角网格重构。从实验结果可以看出,高密度点云简化在重建速度上更快,具有有效的鲁棒性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Industrial Design Applications of Surface Reconstruction Algorithm Based on Three Dimensional Point Cloud Data
At present, rapid reconstruction of amounts of point cloud data is still scarce, so is for the time complexity and space complexity in current methods. This article puts forward an adaptive rasterizing-based triangular mesh reconstruction towards amounts of data simplification reconstruction for storage and transmission. Our measure improves the region expansion: first, macro-estimation method with various points non-difference will obtain 3D grid of side length and separate point cloud data into grid unit. Then, by selecting data points in basic units as seed point and setting triangle side length to approximate positive neighborhood as restriction in order to construct initial triangle grid. Finally, triangle grid reconstruction is completed by layer-by-layer expansion. From experimental results it can be seen, point cloud simplification in high density is faster in reconstruction speed and it has effective robustness.
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