博碩士論文 101022601 詳細資訊




以作者查詢圖書館館藏 以作者查詢臺灣博碩士 以作者查詢全國書目 勘誤回報 、線上人數:11 、訪客IP:54.198.54.142
姓名 賽達明(Lamin O.Ceesay)  查詢紙本館藏   畢業系所 遙測科技碩士學位學程
論文名稱 利用地面光達點雲建置三維房屋模型
(Reconstructing 3D Building Model from Terrestrial LiDAR Point Cloud)
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摘要(中) 近年由於三維房屋模型有大量且廣泛的應用需求,利用光達點雲重建三維房屋模型是其中一項備受關注的技術,並有許多研究單位投入人力與資源於此項技術的演算法開發與流程優化。在諸多尚未解決的議題中,如何提升點雲資料的品質使其重建模型更具完整性即為本研究欲解決的重點之一。例如,處理在儀器掃描的過程中,由於房屋受到自身結構或外在物體的部分遮蔽問題。而使用不完整的光達資料重建三維建物是另一項具挑戰性且實用性高的課題,因此,本研究提出重建表面的空洞填補 (hole-filling) 機制的點雲資料處理演算法,能在資料缺失的部分獲取正確且具有代表性的點雲。另外,本研究所研發自原始點雲中萃取邊緣點與建物邊界線的演算法,亦能有助於利用地面光達點雲重建精確幾何的三維房屋模型。
主要研究內容包含以下步驟,首先以人工移除非房屋的點雲,並將其分割成數個區塊。接著,利用各區塊的點雲資料判斷是否有屬於房屋表面的門或窗等形態的表面間隙 (surface gap) 。偵測到的區塊利用邊界線與表面間隙可再切分成子區塊,其中的邊界線擬合自點雲中萃取的邊緣點。在排除掉表面間隙後,剩餘的間隙則可視為資料缺失所造成的空洞。
為了填補點雲上的空洞,將各區塊的點雲投影至該區塊坐落的主平面上,並將其轉換成網格格式。接著利用最近相鄰內插法填補外包多邊形 (convex hull) 中空白的網格資料,其中網格內若有複數點則以其各點的幾何中心做為代表。最後可建置成多邊形網格表面模型,再以人工方式調整與優化。實驗案例成果顯示,本研究提出之演算法能處理平面或曲面的房屋,且對於地面光達點雲能有更有效率與準確地重建出三維房屋模型。
摘要(英) Reconstructing a 3D building model from LiDAR point cloud has recently gained attention. This is probably due to a large array of potential applications. Several Researches have been conducted on this topic and lots of algorithms have been accordingly developed. Unfortunately, despite the extensive research works, there are still many problems unsolved. One of the main challenging issues yet not thoroughly studied is how to improve the quality of the data in terms of completeness. For example, during the scanning process, parts of the building may not be sampled, either because they are occluded by other objects or they are self-occluded. Reconstructing a building from such incomplete data is not a trivial task. This research presents an approach for accurate reconstruction of geometric 3D building models from terrestrial LiDAR point clouds. As a main focus of the research, an algorithm is proposed for obtaining correct representative data points for the missing parts of an incomplete data set – a task known as hole-filling in surface reconstruction parlance. Further, an effective method for extracting building edge and boundary points from raw point cloud data is developed.
The proposed reconstruction method is in multiple stages. After preprocessing the data to remove unwanted points and outliers, point cloud is segmented into regions. Each resulting segment is checked for containment of surface gaps such as doors and windows. When such a segment is found, it is further decomposed into simpler sub segments using the lines corresponding to the boundaries of the included surface gap(s). The line segments used in decomposition process are obtained from the fitting of lines to the extracted edge points. This systematic exclusion of surface gaps from segments allows all remaining gaps in the data to be treated as holes.
To fill holes, each segment is projected onto its underlying principal plane and converted into regular grid format. The empty grids that are within the convex hull of the projected data are filled using nearest neighbor interpolation. Grids with more than one point are represented by the centroids of the occupants. After these processes, a polygonal mesh surface model is generated which is further adjusted manually and then refined. The results of the test case demonstrate the capability of the proposed approach for effective, accurate and high quality reconstruction of 3D building model from terrestrial LiDAR point clouds. The proposed reconstruction method can handle building with both planar and curved surfaces.
關鍵字(中) ★ 三維房屋模型
★ 地面光達
★ 空洞填補
★ 邊緣點萃取
關鍵字(英) ★ 3D building models
★ Terrestrial LiDAR
★ Hole-filling
★ Edge points extraction
論文目次 CHINESE ABSTRACT i
ABSTRACT ii
ACKNOWLEDGEMENTS iii
LIST OF FIGURES vi
LIST OF TABLES vii
CHAPTER 1. INTRODUCTION 1
1.1. 3D Building Model and Reconstruction 1
1.2. Fundamental Concepts and Background 5
1.3. Motivation 10
1.4. Problem Statement 12
1.5. Research Scope and Approach 14
1.6. Research Focus and Objectives 15
1.7. Thesis Organization 16
CHAPTER 2. LITERATURE REVIEW 17
CHAPTER 3. METHODOLOGY 24
3.1. Data Preprocessing 24
3.1.1. Deleting Unwanted Points 25
3.1.2. Point Cloud Registration 26
3.1.3. Data Resampling 27
3.1.4. Outlier Removal 27
3.2. Point Cloud Segmentation 29
3.2.1. Normal and Curvature Estimation 29
3.2.2. Identifying Boundary and Edge Points 32
3.2.3. Region Growing Segmentation 35
3.3. Hole-Filling 37
3.3.1. Identifying Holes 38
3.3.2. Filling Holes 42
3.4. Model Generation and Manual Editing 44
3.4.1. Model Generation 44
3.4.2. Manual Editing 45
3.5. Model Refinement 45
CHAPTER 4. RESULTS AND DISCUSSION 48
4.1. Result of Segmentation 50
4.2. Result of Hole-filling 51
4.3. Accuracy Assessment 56
CHAPTER 5. CONCLUSION 60
REFERENCES 62
Appendix A. Program for Normal Estimation 71
Appendix B. Program for Hole-filling 72
Appendix C. Procedure Normalestimation 73
Appendix D. Procedure ComputeNormalCurvature 74
Appendix E. Procedure Holefilling 75
Appendix F. Procedure ApplyHoleFilling 76
Appendix G. Procedure ExcludeSurfaceGapsFromSegment 77
Appendix H. Procedure DecomposeSegment 78
參考文獻 Abellán, A., Calvet, J., Vilaplana, J. M., & Blanchard, J. (2010). Detection and spatial prediction of rockfalls by means of terrestrial laser scanner monitoring. Geomorphology, 119(3–4), 162-171. doi: http://dx.doi.org/10.1016/j.geomorph.2010.03.016

Ali, H., Ahmed, B., & Paar, G. (2008). Robust Window Detection from 3D Laser Scanner Data. Paper presented at the CISP 2008 Congress on Image and Signal Processing, 27-30 May 2008, Sayna/Hainan, China.

Alshawa, M., Boulaassal, H., Landes, T., & Grussenmeyer, P. (2009). Acquisition and automatic extraction of facade elements on large sites from a low cost laser mobile mapping system. ISPRS/3DARCH09.

Amenta, N., & Bern, M. (1999). Surface reconstruction by Voronoi filtering. Discrete & Computational Geometry, 22(4), 481-504.

Arayici, Y. (2007). An approach for real world data modelling with the 3D terrestrial laser scanner for built environment. Automation in Construction, 16(6), 816-829.

Arefi, H., & Hahn, M. (2005). A morphological reconstruction algorithm for separating off-terrain points from terrain points in laser scanning data. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 36(3/W19).

Bae, K.-H., & Lichti, D. D. (2008). A method for automated registration of unorganised point clouds. ISPRS Journal of Photogrammetry and Remote Sensing, 63(1), 36-54.

Baltsavias, E. P. (1999). A comparison between photogrammetry and laser scanning. ISPRS Journal of Photogrammetry and Remote Sensing, 54(2–3), 83-94. doi: http://dx.doi.org/10.1016/S0924-2716(99)00014-3

Barber, C. B., Dobkin, D. P., & Huhdanpaa, H. (1996). The quickhull algorithm for convex hulls. ACM Transactions on Mathematical Software (TOMS), 22(4), 469-483.

Barnea, S., & Filin, S. (2008). Keypoint based autonomous registration of terrestrial laser point-clouds. ISPRS Journal of Photogrammetry and Remote Sensing, 63(1), 19-35.

Becker, S., & Haala, N. (2007). Combined feature extraction for façade reconstruction. In: Proceedings of the ISPRS Workshop on Laser Scanning, Espoo, Finland, pp. 241-247.

Becker, S., & Haala, N. (2009). Grammar supported facade reconstruction from mobile lidar mapping. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, Commission XXXVII, Paris, France.

Böhm, J. (2008). Facade detail from incomplete range data. In: Proceedings of the ISPRS Congress, Beijing, China, Vol. 1, p. 2.

Brenner, C. (2005). Building reconstruction from images and laser scanning. International Journal of Applied Earth Observation and Geoinformation, 6(3–4), 187-198. doi: http://dx.doi.org/10.1016/j.jag.2004.10.006

Brodu, N., & Lague, D. (2012). 3D terrestrial lidar data classification of complex natural scenes using a multi-scale dimensionality criterion: Applications in geomorphology. ISPRS Journal of Photogrammetry and Remote Sensing, 68(0), 121-134. doi: http://dx.doi.org/10.1016/j.isprsjprs.2012.01.006

CGAL. Computational Geometry Algorithms Library. http://www.cgal.org

Chang, J. C., Tsai, M. K., Findley, D. J., & Cunningham, C. M. (2012). Infrastructure Investment Protection with LiDAR. North Carolina Department of Transportation Research Project No. 2012-15

Chen, Q., Gong, P., Baldocchi, D., & Xie, G. (2007). Filtering airborne laser scanning data with morphological methods. Photogrammetric Engineering and Remote Sensing, 73(2), 175.

Chen, Y., Ng, M. C., & Wang, Y. Integration of reverse engineering and rapid prototyping with data reduction, Computer Applications in Production and Engineering (Springer, 1997), pp. 289-299.

Davis, J., Marschner, S. R., Garr, M., & Levoy, M. (2002). Filling holes in complex surfaces using volumetric diffusion. In: Proceedings of First International Symposium on 3D Data Processing, Visualization and Transmission, Padua , Italy, pp. 428-441.

El-Hakim, S. F., Beraldin, J.-A., Picard, M., & Godin, G. (2004). Detailed 3D reconstruction of large-scale heritage sites with integrated techniques. Computer Graphics and Applications, IEEE, 24(3), 21-29.

Elberink, S. O., & Vosselman, G. (2011). Quality analysis on 3D building models reconstructed from airborne laser scanning data. ISPRS Journal of Photogrammetry and Remote Sensing, 66(2), 157-165. doi: http://dx.doi.org/10.1016/j.isprsjprs.2010.09.009

Fabio, R. (2003). From point cloud to surface: the modeling and visualization problem. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 34(5), W10.

Förstner, W. 3D-city models: automatic and semiautomatic acquisition methods. In Fritsch, D. & Spiller, R. (Eds.) Photogrammetric Week 99, Wichmann Verlag , Heidelberg, 1999, pp. 291–303.

Frueh, C., Jain, S., & Zakhor, A. (2005). Data Processing Algorithms for Generating Textured 3D Building Facade Meshes from Laser Scans and Camera Images. International Journal of Computer Vision, 61(2), 159-184. doi: 10.1023/B:VISI.0000043756.03810.dd

Galantucci, L., & Percoco, G. (2005). A multilevel approach to edge detection in tessellated point clouds. CIRP Annals-Manufacturing Technology, 54(1), 127-130.

Gressin, A., Mallet, C., Demantké, J., & David, N. (2013). Towards 3D lidar point cloud registration improvement using optimal neighborhood knowledge. ISPRS Journal of Photogrammetry and Remote Sensing, 79(0), 240-251. doi: http://dx.doi.org/10.1016/j.isprsjprs.2013.02.019

Hamann, B. (1994). A data reduction scheme for triangulated surfaces. Computer aided geometric design, 11(2), 197-214.

He, B., Lin, Z., & Li, Y. F. (2013). An automatic registration algorithm for the scattered point clouds based on the curvature feature. Optics & Laser Technology, 46(0), 53-60. doi: http://dx.doi.org/10.1016/j.optlastec.2012.04.027

Hoppe, H., DeRose, T., Duchamp, T., McDonald, J., & Stuetzle, W. (1992). Surface reconstruction from unorganized points. In: Proceedings of 19th Annual Conference on Computer Graphics and Interactive Techniques, ACM SIGGRAPH. Computer Graphics, Vol. 26, pp. 71–78

Hu, J., You, S., Neumann, U., & Park, K. K. (2004). Building modeling from LiDAR and aerial imagery. In: ASPRS′04, Denver, Colorado, USA, Vol. 4, pp. 23-28.

Huber, D. F., & Hebert, M. (2003). Fully automatic registration of multiple 3D data sets. Image and Vision Computing, 21(7), 637-650.

Ikeuchi, K., Oishi, T., Takamatsu, J., Sagawa, R., Nakazawa, A., Kurazume, R., Nishino, K., Kamakura, M., & Okamoto, Y. (2007). The great buddha project: digitally archiving, restoring, and analyzing cultural heritage objects. International Journal of Computer Vision, 75(1), 189-208.

Jin, S., Lewis, R. R., & West, D. (2005). A comparison of algorithms for vertex normal computation. The Visual Computer, 21(1-2), 71-82.

Jones, T. R., Durand, F., & Desbrun, M. (2003). Non-iterative, feature-preserving mesh smoothing. ACM Transactions on Graphics (TOG), 22(3), 943-949.

Ju, T. (2004). Robust repair of polygonal models. ACM Transactions on Graphics (TOG), 23(3), 888-895.

Kaartinen, H., Hyyppä, J., Gülch, E., Vosselman, G., Hyyppä, H., Matikainen, L., Hofmann, A., Mäder, U., Persson, Å., & Söderman, U. (2005). Accuracy of 3D city models: EuroSDR comparison. International archives of photogrammetry, remote sensing and spatial information sciences, 36(3/W19), 227-232.

Kazhdan, M., Bolitho, M., & Hoppe, H. (2006). Poisson surface reconstruction. In: Proceedings of 4th Eurographics Symposium on Geometry Processing (SGP ’06), pp. 61–70.

Kelbe, D., Romanczyk, P., van Aardt, J., & Cawse-Nicholson, K. (2013). Reconstruction of 3D tree stem models from low-cost terrestrial laser scanner data. In: Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Vol. 8731, pp. 06.

Kim, C., & Habib, A. (2009). Object-based integration of photogrammetric and LiDAR data for automated generation of complex polyhedral building models. Sensors, 9(7), 5679-5701.

Kolbe, T., & Bacharach, S. (2006). CityGML: An Open Standard for 3D City Models. Retrieved. 14/ June, 2014, from http://www.directionsmag.com/articles/citygml-an-open-standard-for-3d-citmodels/123103

Kolbe, T. H., Gröger, G., & Plümer, L. CityGML: Interoperable access to 3D city models, Geo-information for disaster management (Springer, 2005), pp. 883-899.

Kuo, C.-C., & Yau, H.-T. (2005). A Delaunay-based region-growing approach to surface reconstruction from unorganized points. Computer-Aided Design, 37(8), 825-835. doi: http://dx.doi.org/10.1016/j.cad.2004.09.011

Liu, Y., & Xiong, Y. (2008). Automatic segmentation of unorganized noisy point clouds based on the Gaussian map. Computer-Aided Design, 40(5), 576-594.

Mederos, B., Velho, L., & de Figueiredo, L. H. (2003). Point cloud denoising. In: Proceeding of SIAM Conference on Geometric Design and Computing.

Meek, D. S., & Walton, D. J. (2000). On surface normal and Gaussian curvature approximations given data sampled from a smooth surface. Computer Aided Geometric Design, 17(6), 521-543.

Meng, X., Currit, N., & Zhao, K. (2010). Ground Filtering Algorithms for Airborne LiDAR Data: A Review of Critical Issues. Remote Sensing, 2(3), 833-860.

Meyer, A., & Marin, P. (2004). Segmentation of 3D triangulated data points using edges constructed with a C1 discontinuous surface fitting. Computer-Aided Design, 36(13), 1327-1336.

Meyer, M., Desbrun, M., Schröder, P., & Barr, A. H. Discrete differential-geometry operators for triangulated 2-manifolds, Visualization and mathematics III (Springer, 2003), pp. 35-57.

Moenning, C., & Dodgson, N. A. (2003). A new point cloud simplification algorithm. In: Proc. Int. Conf. on Visualization, Imaging and Image Processing, pp. 1027-1033.

Moenning, C., & Dodgson, N. A. (2004). Intrinsic point cloud simplification. In: Proceedings of the 14th International Conference on Computer Graphics and Vision (GraphiCon), Moscow, Russia, Vol. 14.

Musialski, P., Wonka, P., Aliaga, D. G., Wimmer, M., Gool, L., & Purgathofer, W. (2013). A Survey of Urban Reconstruction. Computer Graphics Forum, 32(6), 146-177. doi: 10.1111/cgf.12077

Nan, L., Sharf, A., Zhang, H., Cohen-Or, D., & Chen, B. (2010). SmartBoxes for interactive urban reconstruction. ACM Trans. Graph., 29(4), 1-10. doi: 10.1145/1778765.1778830

Page, D. L., Sun, Y., Koschan, A., Paik, J., & Abidi, M. A. (2002). Normal vector voting: crease detection and curvature estimation on large, noisy meshes. Graphical Models, 64(3), 199-229.

Petrie, G., & Toth, C. K. Introduction to Laser Ranging, Profiling, and Scanning. In Shan, J. & Toth, C. K. (Eds.), Topographic Laser Ranging and Scanning: Principles and Processing (CRC Press, Taylor & Francis, 2008a), pp. 1-27.

Petrie, G., & Toth, C. K. Terrestrial Laser Scanners. In J., S. & Toth, C. K. (Eds.), Topographic Laser Ranging and Scanning: Principles and Processing (CRC Press, Taylor & Francis 2008b), pp. 87-126.

Pfeifer, N., Köstli, A., & Kraus, K. (1998). Interpolation and filtering of laser scanner data-implementation and first results. International Archives of Photogrammetry and Remote Sensing, 32, 153-159.

Pu, S. Automatic building modeling from terrestrial laser scanning. In van Oosterom, P., et al. (Eds.), Advances in 3D Geoinformation Systems (Springer, Berlin Heidelberg, 2008), pp. 147-160.

Pu, S., & Vosselman, G. (2006). Automatic extraction of building features from terrestrial laser scanning. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 36(5), 25-27.

Pu, S., & Vosselman, G. (2007). Extracting windows from terrestrial laser scanning. Intl Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 36(Part 3/W52), 320–325.

Pu, S., & Vosselman, G. (2009a). Building facade reconstruction by fusing terrestrial laser points and images. Sensors, 9(6), 4525-4542.

Pu, S., & Vosselman, G. (2009b). Knowledge based reconstruction of building models from terrestrial laser scanning data. ISPRS Journal of Photogrammetry and Remote Sensing, 64(6), 575-584. doi: http://dx.doi.org/10.1016/j.isprsjprs.2009.04.001

Rabbani, T., van den Heuvel, F., & Vosselmann, G. (2006). Segmentation of point clouds using smoothness constraint. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 36(5), 248-253.

Remondino, F. (2011). Heritage Recording and 3D Modeling with Photogrammetry and 3D Scanning. Remote Sensing, 3(6), 1104-1138.

Remondino, F., & Boehm, J. (2013). Theme section “Terrestrial 3D Modeling”. ISPRS Journal of Photogrammetry and Remote Sensing, 76(0), 31-32. doi: http://dx.doi.org/10.1016/j.isprsjprs.2013.01.004

Remondino, F., & El‐Hakim, S. (2006). Image‐based 3D Modelling: A Review. The Photogrammetric Record, 21(115), 269-291.

Rusu, R. B., Marton, Z. C., Blodow, N., Dolha, M., & Beetz, M. (2008). Towards 3D point cloud based object maps for household environments. Robotics and Autonomous Systems, 56(11), 927-941.

Schindler, K., & Bauer, J. (2003). A model-based method for building reconstruction. In: First IEEE International Workshop on Higher-Level Knowledge in 3D Modeling and Motion Analysis, pp. 74-82.

Schneider, R., & Kobbelt, L. (2001). Geometric fairing of irregular meshes for free-form surface design. Computer aided geometric design, 18(4), 359-379.

Sedlacek, D., & Zara, J. Graph Cut Based Point-Cloud Segmentation for Polygonal Reconstruction. In Bebis, G., et al. (Eds.), Advances in Visual Computing (Springer, Berlin Heidelberg, 2009),Vol. 5876, pp. 218-227.

Shewchuk, J. R. (1997). Adaptive precision floating-point arithmetic and fast robust geometric predicates. Discrete & Computational Geometry, 18(3), 305-363.

Slob, S., & Hack, R. 3D Terrestrial Laser Scanning as a New Field Measurement and Monitoring Technique. In Hack, R., et al. (Eds.), Engineering Geology for Infrastructure Planning in Europe (Springer, Berlin Heidelberg, 2004),Vol. 104, pp. 179-189.

Sotoodeh, S. (2007). Hierarchical clustered outlier detection in laser scanner point clouds. Laser 07, p. 383.

Soudarissanane, S., Lindenbergh, R., Menenti, M., &
Teunissen, P. (2011). Scanning geometry: Influencing factor on the quality of terrestrial laser scanning points. ISPRS Journal of Photogrammetry and Remote Sensing, 66(4), 389-399. doi: http://dx.doi.org/10.1016/j.isprsjprs.2011.01.005

Stamos, I., & Allen, P. (2000). 3-D model construction using range and image data. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Vol. 1, pp. 531-536.

Sternberg, H., Kersten, T., Jahn, I., & Kinzel, R. (2004). Terrestrial 3D laser scanning-data acquisition and object modelling for industrial as-built documentation and architectural applications. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 35(B7), 942-947.

Tagliasacchi, A., Zhang, H., & Cohen-Or, D. (2009). Curve skeleton extraction from incomplete point cloud. ACM Trans. Graph., 28(3), 1-9. doi: 10.1145/1531326.1531377

Tarsha-Kurdi, F., Landes, T., Grussenmeyer, P., & Koehl, M. (2007). Model-driven and data-driven approaches using LIDAR data: Analysis and comparison. International Archives of Photogrammetry, Remote Sensing and Spatial Information Systems, 87-92.

Tasdizen, T., Whitaker, R., Burchard, P., & Osher, S. (2002). Geometric surface smoothing via anisotropic diffusion of normals. In: Proceedings of the IEEE conference on Visualization, , pp. 125-132.

Taubin, G. (1995). A signal processing approach to fair surface design. In: Proceedings of the 22nd annual conference on Computer graphics and interactive techniques, pp. 351-358.

Thürrner, G., & Wüthrich, C. A. (1998). Computing vertex normals from polygonal facets. Journal of Graphics Tools, 3(1), 43-46.

Tseng, Y.-H., & Wang, S. (2003). Semiautomated building extraction based on CSG model-image fitting. Photogrammetric Engineering and Remote Sensing, 69(2), 171-180.

Valero, E., Adan, A., & Cerrada, C. (2012a). Automatic construction of 3D basic-semantic models of inhabited interiors using laser scanners and RFID sensors. Sensors, 12(5), 5705-5724.

Valero, E., Adan, A., & Cerrada, C. (2012b). Automatic method for building indoor boundary models from dense point clouds collected by laser scanners. Sensors, 12(12), 16099-16115.

Vollmer, J., Mencl, R., & Mueller, H. (1999). Improved laplacian smoothing of noisy surface meshes. In: Proceedings of the Eurographics, Vol. 18, pp. 131-138.

Vosselman, G., & Dijkman, S. (2001). 3D building model reconstruction from point clouds and ground plans. International Archives of Photogrammetry Remote Sensing and Spatial Information Sciences, 34(3/W4), 37-44.

Vosselman, G., Gorte, B. G., Sithole, G., & Rabbani, T. (2004). Recognising structure in laser scanner point clouds. International archives of photogrammetry, remote sensing and spatial information sciences, 46(8), 33-38.

Wang, J., & Oliveira, M. M. (2003). A hole-filling strategy for reconstruction of smooth surfaces in range images. In: XVI Brazilian Symposium on Computer Graphics and Image Processing( SIBGRAPI 2003), pp. 11-18.

Wang, J., & Oliveira, M. M. (2007). Filling holes on locally smooth surfaces reconstructed from point clouds. Image and Vision Computing, 25(1), 103-113. doi: http://dx.doi.org/10.1016/j.imavis.2005.12.006

Wang, L., Chen, J., & Yuan, B. (2010). Simplified representation for 3D point cloud data. In: 10th International Conference on Signal Processing (ICSP), pp. 1271-1274.

Wang, R., Zhu, X., & Fang, Y. (2009). Three-dimensional building reconstruction from LiDAR point clouds with minimum circum-rectangle. In: Second International Conference on Earth Observation for Global Changes Vol. 7471, pp. 747121-747121.

Watson, C., Chen, S.-E., Bian, H., & Hauser, E. (2011). Three-Dimensional Terrestrial LIDAR for Operational Bridge Clearance Measurements. Journal of Performance of Constructed Facilities, 26(6), 803-811.

Weyrich, T., Pauly, M., Keiser, R., Heinzle, S., Scandella, S., & Gross, M. (2004). Post-processing of scanned 3D surface data. In: Proceedings of the First Eurographics conference on Point-Based Graphics, Switzerland, pp. 85-94.

Wiedemann, A., Hemmleb, M., & Albertz, J. (2000). Reconstruction of historical buildings based on images from the Meydenbauer archives. International Archives of Photogrammetry and Remote Sensing, 33(B5/2; PART 5), 887-893.

Woo, H., Kang, E., Wang, S., & Lee, K. H. (2002). A new segmentation method for point cloud data. International Journal of Machine Tools and Manufacture, 42(2), 167-178.

Xiong, X., Adan, A., Akinci, B., & Huber, D. (2013). Automatic creation of semantically rich 3D building models from laser scanner data. Automation in Construction, 31(0), 325-337. doi: http://dx.doi.org/10.1016/j.autcon.2012.10.006

Yang, B., & Dong, Z. (2013). A shape-based segmentation method for mobile laser scanning point clouds. ISPRS Journal of Photogrammetry and Remote Sensing, 81, 19-30.

Zhan, Q., & Yu, L. Segmentation of LiDAR Point Cloud Based on Similarity Measures in Multi-dimension Euclidean Space,, Advances in Computer Science and Engineering (Springer, Berlin Heidelberg, 2012), pp. 349-357.

Zhang, L., Zhou, R., & Zhou, L. (2003). Model reconstruction from cloud data. Journal of materials processing technology, 138(1), 494-498.

Zhao, H., & Shibasaki, R. (2003). Reconstructing a textured CAD model of an urban environment using vehicle-borne laser range scanners and line cameras. Machine Vision and Applications, 14(1), 35-41.

Zhu, L., Hyyppä, J., Kukko, A., Kaartinen, H., & Chen, R. (2011). Photorealistic Building Reconstruction from Mobile Laser Scanning Data. Remote Sensing, 3(7), 1406-1426.
指導教授 蔡富安(Prof. Fuan Tsai) 審核日期 2014-8-26
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