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Industrial Design Applications of Surface Reconstruction Algorithm Based on Three Dimensional Point Cloud Data

机译:基于三维点云数据的表面重构算法的工业设计应用

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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.
机译:目前,仍然缺乏快速重建点云数据量的方法,对于当前方法中的时间复杂度和空间复杂度也是如此。本文针对存储和传输的简化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。我们的措施改善了区域扩展:首先,具有不同点无差异的宏估计方法将获得边长的3D网格并将点云数据分离为网格单元。然后,通过选择基本单位的数据点作为种子点,并将三角形边长设置为近似正邻域作为约束,以构造初始三角形网格。最后,三角网格的重建是通过逐层扩展来完成的。从实验结果可以看出,高密度点云简化的重建速度更快,并且具有有效的鲁棒性。

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