基于三维形状匹配的点云分割 下载: 1748次
张坤, 乔世权, 周万珍. 基于三维形状匹配的点云分割[J]. 激光与光电子学进展, 2018, 55(12): 121011.
Kun Zhang, Shiquan Qiao, Wanzhen Zhou. Point Cloud Segmentation Based on Three-Dimensional Shape Matching[J]. Laser & Optoelectronics Progress, 2018, 55(12): 121011.
[2] 赵宗泽, 张永军. 基于植被指数限制分水岭算法的机载激光点云建筑物提取[J]. 光学学报, 2016, 36(10): 1028002.
[5] MiyakeS, TodaY, KubotaN, et al.Intensity histogram based segmentation of 3D point cloud using growing neural gas[C]∥International Conference on Intelligent Robotics and Applications, Springer International Publishing, 2016: 335- 345.
[6] Bolles RC, Fischler MA. ARANSAC-based approach to model fitting and its appliAcation to finding cylinders in range data[C]∥International Joint Conference on Artificial Intelligence, 1981: 637- 643.
[8] 胡怀宇, 崔汉国, 代星. 基于区域生长法的散乱点云分区方法[J]. 计算机应用, 2009, 29(10): 2716-2718.
[9] 庞世燕, 刘亚文, 左志奇, 等. 结合区域增长法和TIN边缘分割的建筑物立面几何特征提取[J]. 武汉大学学报(信息科学版), 2015, 40(1): 102-106.
[11] NurunnabiA, BeltonD, WestG. Robust segmentation in laser scanning 3D point cloud data[C]∥International Conference on Digital Image Computing Techniques and Applications (DICTA), 2012: 1- 8.
[14] Velmurugan T, Santhanam T. Computational complexity between K-means and K-medoids clustering algorithms for normal and uniform distributions of data points[J]. Journal of Computer Science, 2010, 6(3): 363-368.
[15] 王帅, 孙华燕, 郭惠超, 等. 激光点云的混合流形谱聚类自适应分割方法[J]. 光学学报, 2017, 37(10): 1011001.
[16] 熊风光, 霍旺, 韩燮, 等. 三维点云中关键点误匹配剔除方法[J]. 光学学报, 2018, 38(2): 0210003.
[17] Yan JZ, Qi MY, Fang LY, et al. Forecast the distribution of urban water point by using improved DBSCAN algorithm[C]∥International Conference on Intelligent System Design and Engineering Applications, 2013: 784- 786.
[18] ZhangX, ZangA, AgamG, et al. Learning from synthetic models for roof style classification in point clouds[C]∥ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, 2014: 263- 270.
[21] Boulch A. Guerry J, le Saux B, et al. SnapNet: 3D point cloud semantic labeling with 2D deep segmentation networks[J]. Computers & Graphics, 2018, 71: 189-198.
[23] PapazovC, BurschkaD. Anefficient RANSAC for 3D object recognition in noisy and occluded scenes[C]∥Asian Conference on Computer Vision, 2010, 6492( 1): 135- 148.
[24] ZulianiM, Kenney CS, Manjunath B S. The multi RANSAC algorithm and its application to detect planar homographies[C]∥IEEE International Conference on Image Processing, 2005, 3: III-153.
[27] Zhang XM, Wan WG, XiaoL, et al. Mean shift clustering segmentation and RANSAC simplification of color point cloud[C]∥International Conference on Audio, Language and Image Processing, 2014: 837- 841.
[30] Overby J, Bodum L, Kjems E, et al. Automatic 3D building reconstruction from airborne laser scanning and cadastral data using Hough transform[J]. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 2004, 34(Part B3): 296-301.
[31] Rabbani T, von den Heuvel F. Efficient Hough transform for automatic detection of cylinders in point clouds[J]. ISPRS Workshop on Laser Scanning, 2005, 3: 60-65.
[32] Tarsha-Kurdi F, Landes F, Grussenmeyer P. Extended RANSAC algorithm for automatic detection of building roof planes from LiDAR data[J]. The Photogrammetric Journal of Finland, 2008, 21(1): 97-109.
[33] Liu YE, Li ZR, HaywardR, et al. Classification of airborne LIDAR intensity data using statistical analysis and Hough transform with application to power line corridors[C]∥Digital Image Computing: Techniques and Applications, 2009: 462- 467.
张坤, 乔世权, 周万珍. 基于三维形状匹配的点云分割[J]. 激光与光电子学进展, 2018, 55(12): 121011. Kun Zhang, Shiquan Qiao, Wanzhen Zhou. Point Cloud Segmentation Based on Three-Dimensional Shape Matching[J]. Laser & Optoelectronics Progress, 2018, 55(12): 121011.