[1] |
IZADI S, KIM D, HILLIGES O, et al. Kinect fusion: Real-time 3-D reconstruction and interaction using a moving depth camera[C]// Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology. Los Angeles, USA: Association of Computing Machinery, 2011: 559-568. |
[2] |
NEWCOMBE R A, IZADI S, HILLIGES O, et al. KinectFusion: Real-time dense surface mapping and tracking[C]//Proceedings of the 2011 10th IEEE International Symposium on Mixed and Augmented Reality. New York, USA: IEEE, 2011: 127-136. |
[3] |
YANG Y T, HUANG G Y, ZHANG K, et al. An improved method of three-dimensional point cloud data segmentation based on curvature constraint[J]. Minicomputer System, 2017, 38 (11): 2573-2579(in Chinese). |
[4] |
WANG Z Y, MA H Ch, XU H G, et al. Fast edge extraction algorithm of massive point cloud[J]. Computer Engineering and Application, 2010, 46(36): 213-215(in Chinese). |
[5] |
BHANU B, LEE S, HO C, et al. Range data processing: Representation of surfaces by edges[C]// Proceedings of the Eighth International Conference on Pattern Recognition. New York, USA: IEEE, 1986: 236-238. |
[6] |
DING Ch J, SUN G, YIN L L, et al. Boundary extraction of scattered point cloud[J]. Computer Technology and Development, 2017, 27(7): 83-86(in Chinese). |
[7] |
BESL P J, JAIN R C. Segmentation through variable-order surface fitting[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 1988, 10(2): 167-192. |
[8] |
STEIN S C, SCHOELER M, PAPON J, et al. Object partitioning using local convexity[C]//Computer Vision and Pattern Recognition. New York, USA: IEEE, 2014: 304-311. |
[9] |
LI R Zh, LIU Y Y, YANG M, et al. 3-D point cloud segmentation based on improved region growth[J]. Progress in Laser and Opto-electronics, 2018, 55(5): 051502(in Chinese). doi: 10.3788/LOP55.051502 |
[10] |
SHI H Y, GUO T, WANG D, et al. Power line suspension point location method based on laser point cloud[J]. Laser Technology, 2020, 44(3): 364-370 (in Chinese). |
[11] |
LIN Y, WANG C, ZHAI D, et al. Toward better boundary preserved supervoxel segmentation for 3-D point clouds[J]. ISPRS Journal of Photogrammetry & Remote Sensing, 2018, 143(9): 39-47. |
[12] |
LI M L, ZONG W P, LI G Y, et al. Extraction of structure line segments from point clouds using voxel-based region growing[J]. Acta Optica Sinica, 2018, 38(1): 0112002 (in Chinese). doi: 10.3788/AOS201838.0112002 |
[13] |
WANG X H, WU L Sh, CHEN H W, et al. Regional segmentation of point cloud data by improved particle swarm optimization fuzzy clustering[J]. Optical Precision Engineering, 2017, 25 (4): 563-573(in Chinese). |
[14] |
GUO B, HUANG X, ZHANG F, et al. Classification of airborne laser scanning data using JointBoost[J]. ISPRS Journal of Photogrammetry & Remote Sensing, 2015, 100(4): 71-83. |
[15] |
LU X, YAO J, TU J, et al. Pairwise linkage for point cloud segmentation[J]. ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences, 2016, 3(3): 201-206. |
[16] |
BIOSCA J M, LERMA J L. Unsupervised robust planar segmentation of terrestrial laser scanner point clouds based on fuzzy clustering methods[J]. ISPRS Journal of Photogrammetry & Remote Sensing, 2008, 63(1): 84-98. |
[17] |
CANG G H, YUE J P. Plane fitting of point clouds based on weighted total least square[J]. Laser Technology, 2014, 38(3): 307-310(in Chinese). |
[18] |
ANAND A, KOPPULA H S, JOACHIMS T, et al. Contextually guided semantic labeling and search for 3-D point clouds[J]. International Journal of Robotics Research, 2011, 32(1): 19-34. |
[19] |
WOLF D, PRANKL J, VINCZE M. Fast semantic segmentation of 3-D point clouds using a dense CRF with learned parameters[C]//2015 IEEE International Conference on Robotics & Automation. New York, USA: IEEE, 2015: 4867-4873. |
[20] |
SHU Z, QI C, XIN S, et al. Unsupervised 3-D shape segmentation and co-segmentation via deep learning[J]. Computer Aided Geometric Design, 2016, 43(3): 39-52. |
[21] |
RICHTSFELD A, MORWALD T, PRANKL J, et al. Segmentation of unknown objects in indoor environments[J]. Intelligent Robots and Systems, 2012, 17(7): 4791-4796. |