三維雷射掃描儀之應用與技術近年來已經逐漸發展成熟，舉凡土木建物建模、古蹟維護、橋梁變形量測、震災勘查、沿海地形退縮及大範圍地形變化監測等，經由三維雷射掃描儀掃描能夠得到待測物表面密緻之點雲資料。然而，目前在處理大量散亂點雲資訊的應用上，較為成熟技術皆屬點雲結合工作及模型建立，且點雲資料並非全部皆有其代表與實用性，故對於點雲資料進一步的分析應用仍待持續發展。本研究的核心分成兩的方向進行，分別為「物件自動辨識」及「地震災後勘查」，前者著重在從散亂的點雲資料中自動辨識特定物件，將原始點雲依造設定之演算策略依序運行，使用區域成長法將散亂點雲資料進行分群分類，再利用邊界提取法、各類演算法提取分群後點群的特徵，最後交叉比對點群特徵推算特定物件是否存在原始點雲中；後者著重在地震災後之勘查，研究因地震作用力造成之結構損傷分析如結構之變形與變位，結構物之表面損傷，因地震造成之邊坡滑動現象紀錄與提取，並提出具體之損傷評估，提出量化的指標。以實驗全尺寸試體掃描資料進行實驗目標物件辨識，結果驗證了本研究提出之物件自動辨識的可行性；本研究於地震災後勘查提出若干量化的評估結果，可用於評估結構物之損傷情形。

In recent year, three-dimensional laser scanner technology with high precision and high efficiency constantly updated. It’s using to modeling the building, heritage conservation, monitor bridge deformation, post-earthquake reconnaissance, coastal terrain retreat monitoring, large-scale terrain change monitoring and etc. It has gradually replaced the traditional measurement technology. However, currently technologies in the large number of scattered point cloud processing applications are concentrated in specific areas such as building models or point cloud data simulation. And the point cloud data will contain a lot of noise to the need for further processing. So the application of point cloud data is in the research and development stage, needs sustainable development. This study is divided into two directions, namely, "automatic object recognition" and "post-earthquake reconnaissance". The first theme focuses on the automatic identification of specific objects from the scattered cloud data, the original point cloud data operated according to the set of calculation strategy. Using of region grow method will be scattered point cloud data classification. And then use all kinds of algorithms, such as boundary extraction method to extract the characteristics of clustering group. The final cross-match point group feature to recognize particular object. The latter theme mainly by investigation the disaster caused by the earthquake. Study the structural damage analysis caused by seismic force, Such as structural deformation and displacement, surface damage, records and Extraction of the earthquake induced large landslide. And put forward specific damage assessment, quantitative indicators. The results show that the feasibility of automatic identification of specific objects, and several quantitative assessment results can be used to assess structural damage.

[1] Aggarwal, A., Booth, H., O'rourke, J., Suri, S., & Yap, C. K. (1989). Finding minimal convex nested polygons. Information and Computation, 83(1), 98-110.
[2] Andrew, A. M. (1979). ANOTHER EFFICIENT ALGORITHM FOR CONVEX HULLS IN TWO DIMENSIONS. Information Processing Letters, 9(5), 216-219.
[3] Armesto-González, J., Riveiro-Rodríguez, B., González-Aguilera, D., & Rivas-Brea, M. T. (2010). Terrestrial laser scanning intensity data applied to damage detection for historical buildings. Journal of Archaeological Science, 37(12), 3037-3047.
[4] Ceylan, A., & Gümüş, M. (2014). Determination of Deformations as a Result of Seismic Loadings on Two-Dimensional Reinforced Concrete Frame via Terrestrial Laser Scanners. Experimental Techniques, 38(2), 19-25.
[5] Farid, R., & Sammut, C. (2012). A relational approach to plane-based object categorisation. Paper presented at the Proceedings of the 2012 Robotics Science and Systems Workshop on RGB-D Cameras. Sydney, Australia.
[6] Gordon, S. J., & Lichti, D. D. (2007). Modeling Terrestrial Laser Scanner Data for Precise Structural Deformation Measurement. Journal of Surveying Engineering, 133(2), 72-80.
[7] Holland, S. M. (May 2008). PRINCIPAL COMPONENTS ANALYSIS (PCA). Retrieved from
[8] Jolliffe, I. T. (2002). Principal component analysis. 2nd ed.
[9] Kashani, A. G., & Graettinger, A. J. (2015). Cluster-Based Roof Covering Damage Detection in Ground-Based Lidar Data. Automation in Construction, 58, 19-27.
[10] Kim, M.-K., Sohn, H., & Chang, C.-C. (2014). Automated dimensional quality assessment of precast concrete panels using terrestrial laser scanning. Automation in Construction, 45, 163-177.
[11] Krein, M., & Smulian, V. (1940). On Regularly Convex Sets in the Space Conjugate to a Banach Space. Annals of Mathematics, 41(3), 556-583.
[12] Lague, D., Brodu, N., & Leroux, J. (2013). Accurate 3D comparison of complex topography with terrestrial laser scanner: Application to the Rangitikei canyon (N-Z). ISPRS Journal of Photogrammetry and Remote Sensing, 82, 10-26.
[13] Lee, H. M., & Park, H. S. (2011). Gage-Free Stress Estimation of a Beam-like Structure Based on Terrestrial Laser Scanning. Computer-Aided Civil and Infrastructure Engineering, 26(8), 647-658.
[14] Lee, J. (2011). Extraction and Regularization of Various Building Boundaries with Complex Shapes Utilizing Distribution Characteristics of Airborne LIDAR Points. ETRI Journal, 33(4), 547-557.
[15] Lichti, D. D., Gordon, S. J., & Tipdecho, T. (2005). Error Models and Propagation in Directly Georeferenced Terrestrial Laser Scanner Networks. Journal of Surveying Engineering, 131(4), 135-142.
[16] Maćkiewicz, A., & Ratajczak, W. (1993). Principal components analysis (PCA). Computers & Geosciences, 19(3), 303-342.
[17] Mei, G. (2016). An alternative algorithm for finding 2D convex hulls on the GPU. Springerplus, 5(1), 696.
[18] Mills, J., & Barber, D. (2004). Geomatics Techniques for Structural Surveying. Journal of Surveying Engineering, 130(2), 56-64.
[19] Pearson, K. (1901). On lines and planes of closest fit to systems of points in space. Philosophical Magazine, 2(6), 559-572.
[20] Poland, C. D., Hill, J., Sharpe, R. L., Soulages, J., Structural Engineers Association Of, C., California, & Office of Emergency, S. (1995). Vision 2000 : performance based seismic engineering of buildings, Sacramento, CA.
[21] Sampath, A., & Shan, J. (2007). Building boundary tracing and regularization from airborne lidar point clouds. Photogrammetric Engineering and Remote Sensing, 73(7), 805-812.
[22] Toussaint, G. T. (1983). SOLVING GEOMETRIC PROBLEMS WITH THE 'ROTATING CALIPERS'. Paper presented at the Proceedings of MELECON 83, Mediterranean Electrotechnical Conference., Athens, Greece.
[23] Truong-Hong, L., Laefer, D. F., Hinks, T., & Carr, H. (2012). Flying Voxel Method with Delaunay Triangulation Criterion for Façade/Feature Detection for Computation. Journal of Computing in Civil Engineering, 26(6), 691-707.
[24] Wang, C., Cho, Y. K., & Kim, C. (2015). Automatic BIM component extraction from point clouds of existing buildings for sustainability applications. Automation in Construction, 56, 1-13.
[25] 牟家昌（2011），「結合二維影像與三維點雲資訊之物體辨識」。國立交通大學電控工程研究所碩士論文，新竹市。
[26] 洪曉竹（2013），「應用空載光達資料自動化萃取建物邊界線」。國立成功大學測量及空間資訊學系碩博士班碩士論文，台南市。
[27] 孫殿柱, 朱昌志, 李延瑞（2009）。散亂點雲邊界特徵快速提取算法。山東大學學報（工學版）第39卷第1期.。
[28] 程鵬飛, 閆浩文, 韓振輝（2008）。一个求解多边形最小面积外接矩形的算法。工程圖學學報， 29（1），頁 122-126。
[29] 黃世涵（2012），「以最小二乘平面套合法進行空載與車載光達點雲套合」。國立交通大學土木工程學系碩士論文，新竹市。
[30] 劉昱緯（2015），「地面雷射掃描於結構幾何辨識與損傷檢測之應用」。國立成功大學土木工程學系碩士論文，台南市。
[31] 潘榮江, 孟祥旭,（2005）。點雲曲面邊界線的提取。全國幾何計算學術會議論文集，GDC2005。
[32] 羅仕東（2011），「整合八分樹結構與適應性網格於光達資料重建室內建物三維模型之研究」。國立中央大學土木工程研究所碩士論文，桃園縣。