基於資料重覆利用和資料分享的概念，本研究提出一個三維建物模型擷取的方法，並以空載光達(airborne LiDAR)所獲取的建物點雲資料作為檢索的依據應用於點雲建模。在Web 2.0的時代，日益俱增的三維模型可以於網路上各種資料分享平台取得。本研究自網路上地理資訊平台以自動下載方式收集各樣式之建物模型建立一個龐大的建物模型資料庫。接著，使用球諧函式(Spherical Harmonic Functions)分別對資料庫內的所有模型以及欲查詢之建物點雲進行編碼。藉著比對資料庫之模型與查詢點雲資料的係數，我們可以從資料庫中擷取出一個最相像的建物模型，並以之為點雲建模的參考樣版。在本研究中，面臨最大挑戰的是點雲資料帶有雜訊及資料缺失的特性。基於此問題，本文提出一對點雲資料及模型資料前處理的程序，透過此前處理過程使三維建物模型擷取系統更加完善。由實驗結果顯示，本文所提之模型擷取演算法有助於快速獲取相似的模型資料，以此資料應用在建物點雲建構上可提升三維數值城市建構與更新之速度。

Based on the concept of data reuse and data sharing, a 3D building model retrieval approach is proposed to extract the best one for a given point clouds. Thanks to the idea of Web 2.0, an increasing number of 3D models are available on the web-based platforms like Google Warehouse. A huge database with a great diversity can be easily constructed from the open sources of these platforms. We aim to build a 3D building model search engine for the demand of quick modeling. This can avoid labor-sensitive point cloud modeling. A complete set of low-frequency spherical harmonic functions is adopted to represent the 3D models in the database and the input point cloud. The most similar model can be extracted from the model database by simply and efficiently matching the spherical harmonic coefficients. Point cloud data acquired by airborne LiDAR is inputted as query to search the similar models from database. Data retrieval by the point cloud data with shape incompleteness and inherent noise is the main challenge to this research. Some data preprocessing procedures will be performed to optimize the retrieval results. The experiment results on the huge 3D database demonstrate the robustness and flexibility of the proposed approach.

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