(3.238.96.184) 您好!臺灣時間:2021/05/12 23:52
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果

詳目顯示:::

我願授權國圖
: 
twitterline
研究生:陳俋臣
研究生(外文):Yi-ChenChen
論文名稱:影像式空載光達點雲編碼應用於三維建築模型擷取
論文名稱(外文):Image-based Airborne LiDAR Point Cloud Encoding for 3D Building Model Retrieval
指導教授:林昭宏林昭宏引用關係
指導教授(外文):Chao-Hung Lin
學位類別:碩士
校院名稱:國立成功大學
系所名稱:測量及空間資訊學系
學門:工程學門
學類:測量工程學類
論文種類:學術論文
論文出版年:2016
畢業學年度:104
語文別:英文
論文頁數:53
中文關鍵詞:點雲編碼三維模型擷取空間直方圖統計數位城市建模
外文關鍵詞:point cloud encoding3D model retrievalspatial histogramcyber city modeling
相關次數:
  • 被引用被引用:1
  • 點閱點閱:176
  • 評分評分:系統版面圖檔系統版面圖檔系統版面圖檔系統版面圖檔系統版面圖檔
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0
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(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。
With the development of Web 2.0 and cyber city modeling, an increasing number of 3D models have been made available on web-based model-sharing platforms with many applications, such as navigation, urban planning, and virtual reality. A 3D model retrieval system based on the concept of data reuse is proposed to retrieve building models similar to a user-specified query. The basic idea is to reuse existing 3D building models instead of reconstructing models from point clouds. Models in databases are generally encoded compactly with a shape descriptor to retrieve models efficiently. However, most of the geometric descriptors in related work are applicable to polygonal models only. 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 using 3D models. Given that the roof of a building is more informative than the other parts in an 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 to encode the models in the database and input point clouds consistently. First, top-view depth images of buildings are generated to represent the geometric surface of a building roof. Second, geometric features are extracted from the depth images according to the height, edge, and plane of the building. Finally, descriptors are extracted by establishing spatial histograms and are used in the 3D model retrieval system. For data retrieval, the models are retrieved by matching the encoding coefficients of point clouds and building models. Experiments on a database that includes about 1,000,000 3D models collected from the Internet are conducted to evaluate data retrieval. Results show that the proposed method is superior to related methods.
摘要 I
Abstract III
致謝 V
List of Tables IX
List of Figures IX
Chapter 1 Introduction 1
Chapter 2 Related Work 6
2.1 Model-based Model Retrieval 6
2.2 View-based Model Retrieval 7
Chapter 3 Methodology 11
3.1 System Overview 11
3.2 Data Preprocessing 12
3.2.1 Building Roof Description 13
3.2.2 Origin Determination 17
3.3 Geometric Features 19
3.3.1 Height Feature 19
3.3.2 Line Feature 20
3.3.3 Eigen Feature 21
3.4 Spatial Histogram 24
3.5 Scale-variant Encoding 27
3.6 Encoding and Indexing 29
Chapter 4 Web-based Model Retrieval System 31
Chapter 5 Experimental Results and Discussion 34
5.1 Test Data and Computational Performance 34
5.2 Encoding Properties 35
5.3 Model Evaluation 38
5.3.1 Encoding of Various LoD models 39
5.3.2 Data Retrieval from a Database 42
Chapter 6 Conclusions and Future Work 49
References 51
Assfalg, J., Bertini, M., Del Bimbo, A., and Pala, P.. Content-based retrieval of 3D objects using spin image signatures. IEEE Trans. on Multimedia, 9(3), pp. 589-599, 2007.
Akgul, C. B., Sankur, B., Yemez, Y. and Schmitt, F.. 3D model retrieval using probability density-based shape descriptors. IEEE Trans. on Pattern Analysis and Machine Intelligence, 31(6), pp. 1117-1133, 2009.
Besl, P. J., and McKay N. D.. Method for registration of 3D shapes. IEEE Trans. on Pattern Analysis and Machine Intelligence, 14(2), pp. 239-256, 1992.
Chen, D. Y., Tian, X. P., and Shen, Y. T.. On visual similarity based 3D model retrieval. Computer Graphics Forum, 22(3), pp. 223-232, 2003.
Chen, L.-C., Teo, T.-A., Rau, J.-Y., Liu, J.-K., and Hsu, W.-C.. Building reconstruction from LIDAR data and aerial imagery. International Geoscience and Remote Sensing Symposium, Vol. 4, pp. 2846, 2005.
Chen, J.-Y., Lin, Lai, H.-J., and Lin, C.-H.. Point cloud modeling using algebraic template. International Journal of Innovative Computing, Information and Control, 7(4), pp. 1521-1532, 2011.
Chen, J.-Y., Lin, C.-H., Hsu, P.-C., and Chen, C.-H.. Point cloud encoding for 3D building model retrieval. IEEE Trans. on Multimedia, 16(2), pp. 337-345, 2014.
Daras, P., Zarpalas, D., Tzovaras, D., and Strintzis, M. G.. Efficient 3D model search and retrieval using generalized 3D radon transforms. IEEE Trans. Multimedia, 8(1), pp. 101V114, 2006.
Funkhouser, T., Min, P., Kazhdan, M., Chen, J., Halderman, A., Dobkin, D., and Jacobs, D.. A search engine for models. ACM Trans. on Graphics, 22(1), pp. 83-105, 2003.
Gross, H., Thoennessen, U.. Extraction of lines from laser point clouds. Symposium of ISPRS Commission III: Photogrammetric Computer Vision PCV06. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. 36, pp. 86-91, 2006.
Gao, Y., Wang, M., Zha, Z.-J., Tian, Q., Dai, Q.-H., and Zhang, N.-Y.. Less is more: efficient 3D object retrieval with query view selection. IEEE Trans. On Multimedia, 13(5), pp. 1007-1018, 2011.
Gao, Y., Tang, J., Hong, R., Yan, S., Dai, Q., Zhang, N., and Chua, T.-S.. Camera constraint-free view-based 3D object retrieval. IEEE Trans. on Image Processing, 21(4), pp. 2269-2281, 2012.
Huang, H., Brenner, C. and Sester, M.. A generative statistical approach to automatic 3D building roof reconstruction from laser scanning data ISPRS Journal of photogrammetry and remote sensing, 79, pp. 29-43, 2013.
Jain, V., and Zhang, H.. A spectral approach to shape-based retrieval of articulated 3D models. Computer-Aided Design, 39(5), pp. 398V407, 2007.
Kim, K., and Shan, J.. Building roof modeling from airborne laser scanning data based on level set approach ISPRS Journal of Photogrammetry and Remote Sensing, 66(4), pp. 484-497, 2011.
Lin, C.-H., Chen, J.-Y., Su, P.-L., and Chen, C.-H.. Eigen-feature analysis of weighted covariance matrices for LiDAR point cloud classification ISPRS Journal of Photogrammetry and Remote Sensing, 94, pp. 70-79, 2014.
Mademlis, A., Daras, P., Tzovaras, D., and Strintzis, M. G.. Ellipsoidal harmonics for 3D shape description and retrieval. IEEE Trans. on Multimedia, 11(8), pp. 1422-1433, 2009.
Mongus, D., Lukač, N., and Žalik, B.. Ground and building extraction from LiDAR data based on differential morphological profiles and locally fitted surfaces. ISPRS Journal of Photogrammetry and Remote Sensing, 93, pp. 145-156, 2014.
Papadakis, P., Pratikakis, I., Perantonis, S., and Theoharis, T.. Efficient 3D shape matching and retrieval using a concrete radialized spherical projection representation. Pattern Recognition, 40(9), pp. 2437-2452, 2007.
Stavropoulos, G., Moschonas, P., Moustakas, K., Tzovaras, D., and Strintzis, M. G.. 3D model search and retrieval from range images using salient features. IEEE Trans. on Multimedia, 12(7), pp. 692-704, 2010.
Tam, K. L., and Lau, W. H.. Deformable model retrieval based on topological and geometric signatures. IEEE Trans. on Visualization and Computer Graphics, 13(3), pp. 470-482, 2007.
Wilson, J. P.. Digital terrain modeling. Geomorphology, 137(1), pp. 107-121, 2012.
Xiong, B., Elberink, S. O., Vosselman, G.. A graph edit dictionary for correcting errors in roof topology graphs reconstructed from point clouds ISPRS Journal of photogrammetry and remote sensing, 93, pp. 227-242, 2014.

連結至畢業學校之論文網頁點我開啟連結
註: 此連結為研究生畢業學校所提供,不一定有電子全文可供下載,若連結有誤,請點選上方之〝勘誤回報〞功能,我們會盡快修正,謝謝!
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top
系統版面圖檔 系統版面圖檔