利用光達資料模塑建物之研究

Building Shaping from LIDAR Data

10.6574/JPRS.2006.11(2).5

張智安(Tee-Ann Teo)；陳良健(Liang-Chien Chen)

光達 ； 等高線 ； 房屋 ； 重建 ； LIDAR ； Contour ； Building ； Reconstruction

航測及遙測學刊

11卷2期（2006 / 06 / 01）

175 - 189

繁體中文

建物模型為數碼城市中重要的空間資料。本研究之目的為使用光達進行建物模塑，主要工作包含建物偵測、建物輪廓線重建及建物屋頂面重建。首先分析光達資料偵測建物區域，接著使用建物等高線產生正規化輪廓線，並以輪廓線內部的光達點雲模塑建物屋頂面。在建物輪廓線重建中，研究中使用光達資料於不同高度之建築物等值線，利用這些等值線產生建物邊界線，研究中假設建築物輪廓線由直線構成且具有正交性，經由正交及共線兩個正規化處理後，可產生規則的輪廓線。在建物屋頂面重建中，使用建物輪廓線內部的光達點雲進行屋頂面之共面分析，研究中使用三角網區域成長法偵測共平面之屋頂面，以相鄰三角網法向量夾角做為共平面門檻，並以最小二乘法回歸出屋頂面之平面，產生房屋模型。研究中使用新竹科學園區之光達資料進行測試，並以1/1000地形圖進行平面評估。因資料特性之差異，本文除了使用屋角點坐標進行平面評估外，並使用建物疊合面積誤差做為評估。本研究以建物模塑為研究重點，研究中針對偵測成功之建物進行模塑，所產生的房屋模型平面優於面積之6%，高程模塑誤差約0.08公尺。

Building model is one of the important spatial information of cyber city. The objective of this investigation is the shaping of the building models from LIDAR data. The major work includes building detection, boundary determination and roof reconstruction. First, we analyze the LIDAR data to detect the building regions. Then, the regular building boundaries are obtained by LIDAR contours. Finally, the roofs of the building are shaped from LIDAR point clouds. In the boundary determination, we use the contours to determine the building boundaries. It is assumed that building boundaries are orthogonal. The orthogonal and collinear constraints are applied in building boundary determination. In the roof reconstruction, a TIN-based region growing is applied to extract the roof planes. The coplanarity and adjacency between triangles are considered for TIN-based region growing. Then, a least squares regression is selected to determine the coplanarity function of planar segment. The proposed method has been tested with LIDAR data of Hsin-Chu Science-based Industrial Park with 1/1000 topomap in evaluation. Due to the data characteristic, we use corner-based and area-based accuracy assessment in the validation. Experimental results indicate that the horizontal error is better than 6% in terms of area, and the shaping error of the reconstructed models is better than 0.08 meter.