本論文提出一套系統，主要應用在3D原始點雲的前處理上，將雜亂無章的點雲作重整，將雜訊點推移至想要的平面，並將點作均勻分布。本系統共分為兩個子系統：1) 應用加權區域最佳投影法於降低取樣 2) 應用雙邊投影重新取樣法於提升取樣。第一個子系統目的為降低點數量以及對點雲作重整，為了提升運算速度，我們將點雲建立為K維樹的資料結構形態，接下來運用加權區域最佳投影法去進行點的吸引與推開，移動雜訊點並使之均勻分布，並且運用k最近鄰近法去除孤立點，形成一組均勻分布無雜訊無孤立點的稀疏取樣點雲。在第二個子系統我們將點的數目新增到與原始點雲數目相當，並且不丟失取樣點雲的無雜訊無孤立點且均勻分布的特性，在第二子系統我們採用的是雙邊投影的演算法去作提升取樣，首先取其中一點與其周圍鄰近點並計算其投射基準點，再依據其基準點周圍父母點的法向量進行投射，投射至經過父母點的延伸面上，成為新增點，反覆迭代計算。因其演算法取用周圍父母點的法向量為參考，因此能妥善保留取樣點雲的特徵。最後本系統套用重新取樣的加權區域最佳投影法的架構與其重新取樣的方法去做整理系統的加速。

This thesis presents a system that mainly used in the 3D unorganized point cloud preprocessing. This system can consolidate raw point cloud data, attract noise points to the desired plane, and repulse points for the uniform distribution. The system is divided into two subsystems: 1) downsampling using WLOP (weighted locally optimal projection), 2) upsampling using bilateral projection resampling. In order to improve the speed of the process, we set the point cloud as the data structure of the K-dimensional tree, and then use the WLOP method to proceed attraction and repulsion. In order to get an organized point cloud, we move the noise points and make it evenly distributed, we also use k nearest neighbor method to remove the outlier points. In the second subsystem we add the number of points to the number of the original point cloud and not losing characteristics of no noise, no outlier and evenly distributed. In the second subsystem we are using bilateral projection method. First, we select one point and calculate the base point according to its k nearest neighbor points, and then project the base point to the extended plane of parent points according to normal vector of parent points. Finally, the projected point becomes the new point, then we iterate this process until we have enough points. Because of its algorithm taking the parent points of the normal vector as a reference, so we can properly keep the characteristics of the sample point cloud. At the end, this system applies the resampling WLOP and its resampling method to speed up the system.

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