影像式空載光達點雲編碼應用於三維建築模型擷取

Image-based Airborne LiDAR Point Cloud Encoding for 3D Building Model Retrieval

陳俋臣(Yi-Chen Chen) ； 林昭宏(Chao-Hung Lin)

《航測及遙測學刊》 23卷1期 (2018/03) Pp. 1-13

https://doi.org/10.6574/JPRS.201803_23(1).0001

摘要

隨著網際網路2.0技術與數位城市模型建置的發展，愈來愈多三維數位模型的創造者，在網路形式的資料共享平台上分享作品，這些三維數位模型可以應用在許多領域，例如：導航、都市規劃與虛擬實境。以往重建三維建築模型使用的是攝影測量或點雲資料，但為使資源再利用，本文提出一個三維數位模型的擷取系統。首先從網際網路收集約100萬個三維數位模型，接著用空載光達系統獲取的三維建築模型點雲，作為查詢的輸入資料，以擷取資料庫中與輸入資料的幾何形狀相似的三維數位模型，再利用擷取出的三維建築模型，有效率地建造數位城市模型。為了有效地擷取三維數位模型，常見的做法是使用一個幾何形狀描述子，來編碼資料庫中的三維數位模型，這樣的做法適用於使用三維數位模型作為查詢資料。然而我們提出的系統，是利用空載光達點雲作為查詢資料。空載光達點雲具有稀疏、雜訊以及取樣不完整的特性，相關研究的方法無法直接適用於我們的系統，因此我們提出以深度影像和空間直方圖統計為基礎的編碼方式：利用深度影像描述建築物屋頂面的表面起伏，配合幾何特徵的萃取，將幾何特徵透過空間直方圖統計得到的係數，作為編碼結果的表達方式。本研究的編碼方式，可以降低由空載光達點雲資料雜訊衍生的問題，和側面與底面不完整取樣，所帶來的編碼困難度。藉由三維建築模型的細節層次(Level of Detail, LoD)測試，以及三維數位模型資料庫的資料擷取測試，可證本文提出的方法優於其他相關的方法，並說明由空載光達點雲資料的編碼擷取三維建築模型，以建構數位城市模型的可行性。

關鍵字

點雲編碼 ； 三維模型擷取 ； 空間直方圖統計 ； 數位城市建模

並列摘要

With the development of Web 2.0 and cyber city modeling, an increasing number of 3D models have been available on web-based model-sharing platforms with many applications such as navigation, urban planning, and virtual reality. Based on the concept of data reuse, a 3D model retrieval system is proposed to retrieve building models similar to a user-specified query. The basic idea behind this system is to reuse these existing 3D building models instead of reconstruction from point clouds. To efficiently retrieve models, the models in databases are compactly encoded by using a shape descriptor generally. However, most of the geometric descriptors in related works are applied to polygonal models. In this study, the input query of the model retrieval system is a point cloud acquired by Light Detection and Ranging (LiDAR) systems because of the efficient scene scanning and spatial information collection. Using Point clouds with sparse, noisy, and incomplete sampling as input queries is more difficult than that by using 3D models. Because that the building roof is more informative than other parts in the airborne LiDAR point cloud, an image-based approach is proposed to encode both point clouds from input queries and 3D models in databases. The main goal of data encoding is that the models in the database and input point clouds can be consistently encoded. Firstly, top-view depth images of buildings are generated to represent the geometry surface of a building roof. Secondly, geometric features are extracted from depth images based on height, edge and plane of building. Finally, descriptors can be extracted by spatial histograms and used in 3D model retrieval system. For data retrieval, the models are retrieved by matching the encoding coefficients of point clouds and building models. In experiments, a database including about 1,000,000 3D models collected from the Internet is used for evaluation of data retrieval. The results of the proposed method show a clear superiority over related methods.

並列關鍵字

Point Cloud Encoding ； 3D Model Retrieval ； Spatial Histogram ； Cyber City Modeling