光達因為能夠快速的取得大量且精準的空間資訊，因此目前被大量的應用在數碼城市的建構及建築物的重建方面。傳統上點雲多以不規則三角網建立的三維模型，該方法雖然精細度高但是其所產生的三角形數量龐大，在運算上及儲存上較不符合效率，再加上建築物點雲資料較具規則且多由平面組成，因此本研究希望藉由八分樹結構化萃取點雲中的平面，並與適應性網格結構結合，以處理建築物中平面交接或不規則物件，期望提出高效率且自動化之三維模型建立方法。本研究提出之演算法係以八分樹結構化為主體，先試圖從點雲資料中萃取平面，若無法萃取出平面時則將點雲以八分樹分割後再次萃取，並加入初始網格之建構、點雲集中度之檢查、點雲範圍判定、以及切面範圍之修正等考量以提昇重建模型之正確性，接著訂定一個門檻值，將網格尺寸達到門檻值之點雲資料改以適應性網格進行處理，並定義網格代表點之選取規則以正確描述物體之邊緣測試結果顯示本研究可有效結合兩種方法，並以自動化的方式利用點雲資料重建三維模型。

Lidar is widely used for the applications of the Cyber city construction and 3D reconstruction of buildings, because it is capable of fast and accurate data acquisition from distant objects. Traditionally, the Triangulated Irregular Network (TIN) is used to create 3D models from a point cloud. Although the generated models are realistic, TIN creates a huge number of triangles and causes inefficient computation and data storage. Furthermore, one of the most significant properties of buildings is that they are mostly composed of planes and have more regular shapes than other types of objects. Therefore, this research aims to integrate octree structure, which is effective in extracting planes in a point cloud, and grid adaptation model, which is effective for reconstructing detailed parts of an object, for the automated generation of 3D models. In the first part, the planes are extracted, if existed, based on the points in a cell. If a plane cannot be found, the cell is split using an octree structure. It is found that considering initial cell sizes, point distribution in the cell, boundaries of the points, and adjustment for the point distance will improve the correctness of the 3d models. In the second part, to avoid the errors caused by small cell sizes, the splitting stops when the size of a cell is smaller than a threshold and the points are then transferred to the grid adaption models. The main consideration in the grid adaptation model is the selection of the representative point based on the relationship between adjacent cells. The planes found in the both parts are combined for the final model. Test results show that the octree structure and the grid adaptation model are successfully combined and can be used for automated 3D reconstruction for buildings.

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