光達具有快速獲取大量三維坐標點資訊的優點，可提供每秒數千點至數萬點的觀測數據，這些大量的點雲分佈幾何隱含豐富的物面資訊。然而這些物面特徵資訊並非直接的幾何描述，必須轉換為數學函式或向量描述資料，才能成為可直接利用的顯性資訊。本研究利用區域成長法的概念，搭配面擬合的計算，合併具有共面特性的點群，進而萃取出點雲中的面特徵。在區域成長的過程中，以鄰近擬合面的法向量夾角以及掃描點到擬合面的距離為合併的判斷依據，則經由區域成長運算所集結的同類元素形成連結的區域可視為一個面特徵，所發展的面重建方法可重建點雲中的平面、球面、圓柱面及多項式曲面等面特徵。針對幾種不同地物的地面光達點雲資料進行實驗，得到相當良好的面重建成果，應用於空載光達點雲上，亦能成功地萃取屋頂面。實驗中亦針對面重建過程中所需要設定的預定參數，包括點雲的切割網格尺寸、角度門檻值、距離門檻值及種子的成長位置進行探討，並以半自動化的方式由點雲資料來獲取預定參數設置的相關資訊，以提供設定之參考依據。

Lidar (Laser Scanner) is capable of collecting a large number of 3D points, in which abundant surface features are implied in the distribution of point cloud data. However, these surface features should be extracted to from explicit information, i.e., it is necessary to transfer the point cloud data into mathematical expressions or vector data descriptions. The proposed algorithm of surface reconstruction is based on the schemes of surface growing and surface feature fitting. It merges the co-plan points and extracts surface features from point cloud data. There are two factors to conduct the growing process: the angle between two normal vectors of adjacent patches and the distance of the point from the growing surface. Every merged cell is considered as a small patch, then the connect areas by region growing regarded as a surface feature. The reconstructive surface features in the proposed method include planar, spherical,cylindrical, and polynomial surface features. The experimental data include ground-based Lidar and airborne Lidar. The overall results show the successful application examples of the proposed algorithm. In the experiment, the initial parameters such as grid sizes, threshold of angle, threshold of distance and the growing seed position are also discussed and extracted from point cloud data using a semi- automatic method. The results of reconstructive surface provide points cluster with the same surface characteristics and fitting parameter of the surface features. The extracted surface features will be useful for 3D modeling.