結合多元資料重建三維房屋模型

Integrating Multi-source Data for the Generation of 3-D Building Models

陳良健(Liang-Chien Chen) ； 李唐宇(Tang-Yu Li)

《航測及遙測學刊》 13卷3期 (2008/09) Pp. 159-168

https://doi.org/10.6574/JPRS.2008.13(3).1

摘要

三維房屋模型的建立在數碼城市中不可或缺。本研究以資訊融合方式，結合向量圖、空載光達資料以及航照資料，重建三維房屋模型。重建的重點包括圓弧屋頂和非圓弧屋頂之房屋。研究中使用向量資料提供房屋外部輪廓，因此研究重點爲房屋模型重建。由於圓弧屋頂之房屋在影像上並無明顯特徵，因此研究以空載光達資料之三維資訊來描述圓弧屋頂。而爲了彌補光達點雲密度可能不足之情形，研究使用航照影像獲取非圓弧形屋中平頂屋內部結構直線段。研究步驟，首先對三種資料分別進行前處理的作業，接著萃取房屋內部之光達點雲，由不同的面方程式進行擬合，將不同之屋頂形狀分類。研究中以平面方程式所擬合的小單元描述圓弧頂房屋，非圓弧頂者，山型屋以面相交方式求取屋脊線，平頂屋部份結合空載光達資料與影像偵測階梯線。最後利用分割－合併－模塑方法模塑產生房屋模型。本研究測試區位於新竹科學園區。向量圖比例尺爲1：1000，空載光達資料點雲密度爲1.5pts/平方公尺，航照影像之空間解析力爲0.12 m。研究成果顯示，屋頂面分類成功率可達80%，模型重建正確率爲85%。房屋輪廓部分均方根誤差於X方向爲0.51 m，Y方向爲0.41 m，模塑誤差爲0.19 m。

關鍵字

光達 ； 航照影像 ； 向量圖 ； 模型重建 ； 房屋模型

並列摘要

Three dimensional building models are the indispensable component in the cyber city. This investigation integrates 2-D maps, LIDAR data, and aerial images for building modeling. This research handles flat, gable, and cambered roofs. Vector maps are used to locate the building boundaries in this research. Since a cambered roof does not have significant features in the image space, we use the LIDAR point clouds to model it. Because the density of the LIDAR point clouds might not be sufficient to reconstruct the internal facets of buildings, we employ aerial images. In the first step, the data preprocessing encloses the polylines of the maps then extract the point clouds that belong to a building. After filtering the point clouds, we fit the data by different surface functions. Through the roof hypothesis by employing point clouds, the camber roofs are parameterized. For non-camber roofs, the ridges of gable roofs will be intercepted by the two inclined planes. The step-edges of flat roofs are obtained by combining point clouds and image features. Then the lines are projected to the object space by ray-tracing. Finally, we shape the models by SMS method. The test site is in the Industrial Technology Research Institute of Hsin-chu. The vector maps are with a scale of 1:1,000. The point density of LIDAR data is 1.5 points/m^2, and the spatial resolution of aerial image is 0.12 m. The result indicates the successful rate is 80% in building classification while the fully reconstruction rate is 85%. The RMSE of building boundaries are 0.51 m and 0.41 m in X and Y directions, respectively. The shaping error is 0.19 m.

並列關鍵字

LIDAR ； aerial image ； 2D map ； building reconstruction ； building model