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  "contentMarkdown": "# 三维重建学习笔记整理\n\n# 三维重建学习笔记整理\n\n> 创建时间：2019/12/27 16:31\n\n## 相关概念\n\n**表面重建** 问题可以描述为： **给定位于或接近未知表面U 的点集X ，构造表面S以逼近U** 。\n**点集X** ：通常为物体表面点的坐标，同时还可能含有测量点方位、点集法向量、测量准确性等信息\n**点云数据** 通过某种测量手段得到的仅含物体表面坐标信息的无序点集，通常称为点云数据或空间散乱点。\n\n## 三维重建步骤及简介\n\n#### 三维数据采集\n\n  * 接触式测量：坐标测量机测量法\n    * 优点：测量精度高，对表面色泽无特殊要求，适合测量高反射系数的表面\n    * 缺点：的测量效率低，不适合测量具有复杂内腔、液态以及质软的物体\n  * 非接触式测量：指借助光、声、磁等手段测得物体表面点坐标的方法\n    * 优点：测量迅速，数据量大， MRI和CT测量方法不仅可以得到表面数据，还可以得到被测对象内部数据\n    * 缺点：测量精度比接触式测量低\n\n#### 三维数据配准\n\n点云配准是指两个或者一系列相关点云数据之间通过刚性变换统一到一个公共坐标系下的过程。\n\n#### 配准后模型的预处理\n\n##### 滤波去噪\n\n由于测量方法限制以及被测对象表面特性等因素，在得到的点云数据中不可避免的会含有噪声。噪声属于高频干扰，可以采用信号处理中的低通滤波原理来降低噪声。\n\n##### 平滑\n\n原因：激光扫描仪等设备扫描物体，尤其是比较小的物体时，往往会有测量误差。这些误差所造成的不规则数据如果直接拿来曲面重建的话，会使得重建的曲面不光滑或者有漏洞。这种不规则数据很难用前面我们提到过的统计分析等滤波方法消除，所以为了建立光滑完整的模型必须对物体表面进行平滑处理和漏洞修复。\n方法：用重采样来平滑\n“移动最小二乘”（MLS， Moving Least Squares ）法实现\n\n```xml\npublic class AGenericClass<T> where T : IComparable<T> { }\n\n```\n\n##### 估计点云的表面法线（normal）\n\n  1. 使用曲面重建方法，从点云数据中得到采样点对应的曲面，然后再用曲面模型计算其表面的法线\n  2. 直接使用近似值直接从点云数据集推断出曲面法线（从该点最近邻计算的协方差矩阵的特征向量和特征值的分析）\n\n#### 表面重建（网格化）\n\n将无序点云以一定的法则联结并在点与点之间填充面片，使基于点的三维数据变成基于面的三维数据，从而尽可能的逼近原始目标物。三角形是最常用的面片形式。\n1\\. 凸包算法\n2\\. Ear Clipping三角化算法\n3\\. 贪婪投影三角化算法\n4\\. Marching Cubes（移动立方体）算法\n5\\. 泊松曲面重建算法\n"
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