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Fast self-repairing region growing surface reconstruction algorithm for unorganised point cloud data

机译:快速自修复区域生长面部重建云云数据

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This paper proposes a fast self-repairing projection-based surface reconstruction algorithm for both closed-form and free-form data sets in region-growing principle, which generates triangles between reference point and its neighbours according to their status and positions on the tangent plane. In this work, the whole framework of meshing and related concepts is outlined first, then the triangulating procedure is summarised to seven different cases according to the status and position of points, and the corresponding triangulating details are presented. To eliminate the potential missing triangles derived from the triangulating procedure, a data structure Single Edge Index Table (SEIT) is developed to track all the boundaries of the generated triangular mesh and dynamically updated along with the triangle forming. After the triangulation, a quick depth traverse on SEIT is carried out to detect all the local holes in reconstructed mesh, followed by a hole filling procedure to wipe off the holes within predefined size. Experiments validate that proposed algorithm reconstructs better mesh for both closed-form and free-form point clouds, and achieves high efficiency.
机译:本文提出了一种快速的自修复基于封闭的投影基于闭合的投影基于封闭形式和自由形式数据集的基于区域 - 生长原理,其根据切线平面上的状态和位置在参考点及其邻居之间产生三角形。在这项工作中,首先概述了网格化和相关概念的整个框架,然后根据点的状态和位置来总结到七种不同的情况,并且呈现了相应的三角形细节。为了消除从三角测量过程导出的潜在缺失三角形,开发了数据结构单边缘索引表(SEIT)以跟踪所生成的三角网格的所有边界,并随着三角形形成动态更新。在三角测量之后,对Seit进行快速深度横向,以检测重建网格中的所有局部孔,然后是孔填充过程以擦除预定尺寸内的孔。实验验证该算法为闭合形式和自由形状云进行更好的网格,实现高效率。

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