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2020, 2(3): 175-212 Published Date:2020-6-20

DOI: 10.1016/j.vrih.2020.05.003

Urban 3D modeling using mobile laser scanning: a review

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Abstract:

Mobile laser scanning (MLS) systems mainly comprise laser scanners and mobile mapping platforms. Typical MLS systems can acquire three-dimensional point clouds with 1-10cm point spacings at a normal driving or walking speed in streets or indoor environments. The efficiency and stability of these systems make them extremely useful for application in three-dimensional urban modeling. This paper reviews the latest advances of the LiDAR-based mobile mapping system (MMS) point cloud in the field of 3D modeling, including LiDAR simultaneous localization and mapping, point cloud registration, feature extraction, object extraction, semantic segmentation, and processing using deep learning. Furthermore, typical urban modeling applications based on MMS are also discussed.
Keywords: 3D Modeling ; MMS ; LIDAR ; Urban

Cite this article:

Cheng WANG, Chenglu WEN, Yudi DAI, Shangshu YU, Minghao LIU. Urban 3D modeling using mobile laser scanning: a review. Virtual Reality & Intelligent Hardware, 2020, 2(3): 175-212 DOI:10.1016/j.vrih.2020.05.003

1. El-Sheimy N. The development of VISAT: a mobile survey system for GIS applications. 1996

2. Thompson J, Sorvig K. Sustainable landscape construction: a guide to green building outdoors, second edition. 2008

3. Li D R. Mobile mapping technology and its applications. Geospatial Information, 2006, 4(4): 1–5 (in Chinese)

4. Kukko A, Kaartinen H, Hyyppä J, Chen Y W. Multiplatform mobile laser scanning: usability and performance. Sensors, 2012, 12(9): 11712–11733 DOI:10.3390/s120911712

5. Olsen M J. Guidelines for the use of mobile LIDAR in transportation applications. Transportation Research Board, 2013

6. Glennie C. Rigorous 3D error analysis of kinematic scanning LIDAR systems. Journal of Applied Geodesy, 2007, 1(3): 147–157 DOI:10.1515/jag.2007.017

7. Feng Y M, Gu S F, Shi C, Rizos C. A reference station-based GNSS computing mode to support unified precise point positioning and real-time kinematic services. Journal of Geodesy, 2013, 87(10/11/12): 945–960 DOI:10.1007/s00190-013-0659-7

8. Jeffrey C. An introduction to GNSS: GPS, GLONASS, Galileo and other global navigation satellite systems. NovAtel, 2010

9. Martinsanz G P. State-of-the-Art Sensors Technology in Spain 2017. MDPI, 2018

10. Zhang J, Singh S. Laser-visual-inertial odometry and mapping with high robustness and low drift. Journal of Field Robotics, 2018, 35(8): 1242–1264 DOI:10.1002/rob.21809

11. Besl P, McKay N. Method for registration of 3-D shapes. SPIE, 1992

12. Biber P, Strasser W. The normal distributions transform: a new approach to laser scan matching. In: Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat No03CH37453). 2003, 2743–2748 DOI:10.1109/iros.2003.1249285

13. Zhang J, Singh S. Visual-lidar odometry and mapping: low-drift, robust, and fast. In: 2015 IEEE International Conference on Robotics and Automation (ICRA). Seattle, WA, USA, IEEE, 2015, 2174–2181 DOI:10.1109/icra.2015.7139486

14. Fang Z, Scherer S. Experimental study of odometry estimation methods using RGB-D cameras. In: 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems. Chicago, IL, USA, IEEE, 2014 DOI:10.1109/iros.2014.6942632

15. Maurelli F, Droeschel D, Wisspeintner T, May S, Surmann H. A 3D laser scanner system for autonomous vehicle navigation. In: 2009 International Conference on Advanced Robotics. 2009, 1–6

16. Davison A J, Reid I D, Molton N D, Stasse O. MonoSLAM: real-time single camera SLAM. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007, 29(6): 1052–1067 DOI:10.1109/tpami.2007.1049

17. Mur-Artal R, Montiel J M M, Tardos J D. ORB-SLAM: a versatile and accurate monocular SLAM system. IEEE Transactions on Robotics, 2015, 31(5): 1147–1163 DOI:10.1109/tro.2015.2463671

18. Forster C, Pizzoli M, Scaramuzza D. SVO: Fast semi-direct monocular visual odometry. In: 2014 IEEE International Conference on Robotics and Automation (ICRA). Hong Kong, China, IEEE, 2014, 15–22 DOI:10.1109/icra.2014.6906584

19. Labbe M, Michaud F. Appearance-based loop closure detection for online large-scale and long-term operation. IEEE Transactions on Robotics, 2013, 29(3): 734–745 DOI:10.1109/tro.2013.2242375

20. Labbe M, Michaud F. Online global loop closure detection for large-scale multi-session graph-based SLAM. In: 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems. Chicago, IL, USA, IEEE, 2014, 2661–2666 DOI:10.1109/iros.2014.6942926

21. Geneva P, Eckenhoff K, Yang Y L, Huang G Q. Lips: lidar-inertial 3D plane slam. In: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Madrid, IEEE, 2018, 123–130 DOI:10.1109/iros.2018.8594463

22. Abolfazli Esfahani M, Wang H, Wu K Y, Yuan S H. AbolDeepIO: a novel deep inertial odometry network for autonomous vehicles. IEEE Transactions on Intelligent Transportation Systems, 2020, 21(5): 1941–1950 DOI:10.1109/tits.2019.2909064

23. Qin T, Li P L, Shen S J. VINS-mono: a robust and versatile monocular visual-inertial state estimator. IEEE Transactions on Robotics, 2018, 34(4): 1004–1020 DOI:10.1109/tro.2018.2853729

24. Qin T, Cao S Z, Pan J, Shen S J. A general optimization-based framework for global pose estimation with multiple sensors. 2019

25. Segal A, Haehnel D, Thrun S. Generalized-ICP. In: Robotics: Science and Systems V, Robotics: Science and Systems Foundation, 2009, 2(4): 435 DOI:10.15607/rss.2009.v.021

26. Pandey G, Savarese S, McBride J R, Eustice R M. Visually bootstrapped generalized ICP. In: 2011 IEEE International Conference on Robotics and Automation. Shanghai, China, IEEE, 2011, 2660–2667 DOI:10.1109/icra.2011.5980322

27. Andreasson H, Triebel R, Burgard W. Improving plane extraction from 3D data by fusing laser data and vision. In: 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems. Edmonton, Alta, Canada, IEEE, 2005, 2656–2661 DOI:10.1109/iros.2005.1545157

28. Joung J H, An K H, Kang J W, Chung M J, Yu W. 3D environment reconstruction using modified color ICP algorithm by fusion of a camera and a 3D laser range finder. In: 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems. St. Louis, MO, USA, IEEE, 2009, 3082–3088 DOI:10.1109/iros.2009.5354500

29. Men H, Gebre B, Pochiraju K. Color point cloud registration with 4D ICP algorithm. In: 2011 IEEE International Conference on Robotics and Automation. Shanghai, China, IEEE, 2011, 1511–1516 DOI:10.1109/icra.2011.5980407

30. Graeter J, Wilczynski A, Lauer M. LIMO: lidar-monocular visual odometry. In:2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Madrid, IEEE, 2018, 7872–7879 DOI:10.1109/iros.2018.8594394

31. Ye H Y, Chen Y Y, Liu M. Tightly coupled 3D lidar inertial odometry and mapping. In: 2019 International Conference on Robotics and Automation (ICRA). Montreal, QC, Canada, IEEE, 2019, 3144–3150 DOI:10.1109/icra.2019.8793511

32. Kuindersma S, Deits R, Fallon M, Valenzuela A, Dai H K, Permenter F, Koolen T, Marion P, Tedrake R. Optimization-based locomotion planning, estimation, and control design for the atlas humanoid robot. Autonomous Robots, 2016, 40(3): 429–455 DOI:10.1007/s10514-015-9479-3

33. Yu S J, Sukumar S R, Koschan A F, Page D L, Abidi M A. 3D reconstruction of road surfaces using an integrated multi-sensory approach. Optics and Lasers in Engineering, 2007, 45(7): 808–818 DOI:10.1016/j.optlaseng.2006.12.007

34. Hervieu A, Soheilian B. Semi-automatic road/pavement modeling using mobile laser scanning. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, 2013, II-3/W3: 31–36 DOI:10.5194/isprsannals-ii-3-w3-31-2013

35. Marton Z C, Rusu R B, Beetz M. On fast surface reconstruction methods for large and noisy point clouds. In: 2009 IEEE International Conference on Robotics and Automation. Kobe, IEEE, 2009, 3218–3223 DOI:10.1109/robot.2009.5152628

36. Lipman Y, Cohen-Or D, Levin D, Tal-Ezer H. Parameterization-free projection for geometry reconstruction. ACM Transactions on Graphics, 2007, 26(3): 22 DOI:10.1145/1276377.1276405

37. Nealen A, Igarashi T, Sorkine O, Alexa M. Laplacian mesh optimization. In: Proceedings of the 4th international conference on Computer graphics and interactive techniques in Australasia and Southeast Asia. Kuala Lumpur, Malaysia, New York, USA, ACM Press, 2006, 381–389 DOI:10.1145/1174429.1174494

38. Sarkar K, Varanasi K, Stricker D. Learning quadrangulated patches for 3D shape parameterization and completion. In: 2017 International Conference on 3D Vision (3DV). Qingdao, IEEE, 2017, 383–392 DOI:10.1109/3dv.2017.00051

39. Zhao W, Gao S M, Lin H W. A robust hole-filling algorithm for triangular mesh. The Visual Computer, 2007, 23(12): 987–997 DOI:10.1007/s00371-007-0167-y

40. Davis J, Marschner S R, Garr M, Levoy M. Filling holes in complex surfaces using volumetric diffusion. In: Proceedings. First International Symposium on 3D Data Processing Visualization and Transmission, Padova, Italy, IEEE Comput. Soc, 2002, 428–441 DOI:10.1109/tdpvt.2002.1024098

41. Kroemer O, Ben Amor H, Ewerton M, Peters J. Point cloud completion using extrusions. In: 2012 12th IEEE-RAS International Conference on Humanoid Robots (Humanoids 2012). Osaka, Japan, IEEE, 2012, 680–685 DOI:10.1109/humanoids.2012.6651593

42. Figueiredo R, Moreno P, Bernardino A. Automatic object shape completion from 3D point clouds for object manipulation. In: Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications. Porto, Portugal. SCITEPRESS-Science and Technology Publications, 2017, 565–570 DOI:10.5220/0006170005650570

43. Sipiran I, Gregor R, Schreck T. Approximate symmetry detection in partial 3D meshes. Computer Graphics Forum, 2014, 33(7): 131–140 DOI:10.1111/cgf.12481

44. Wolf D, Howard A, Sukhatme G S. Towards geometric 3D mapping of outdoor environments using mobile robots. In: 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems. Edmonton, Alta, Canada, IEEE, 2005, 1507–1512 DOI:10.1109/iros.2005.1545152

45. Thrun S, Wegbreit B. Shape from symmetry. In: Tenth IEEE International Conference on Computer Vision (ICCV'05). Beijing, China, IEEE, 2005, 1824–1831 DOI:10.1109/iccv.2005.221

46. Mitra N J, Guibas L J, Pauly M. Partial and approximate symmetry detection for 3D geometry. ACM Transactions on Graphics, 2006, 25(3): 560–568 DOI:10.1145/1141911.1141924

47. Xu K, Zhang H, Tagliasacchi A, Liu L G, Li G, Meng M, Xiong Y S. Partial intrinsic reflectional symmetry of 3D shapes. ACM Transactions on Graphics, 2009, 28(5): 1–10 DOI:10.1145/1618452.1618484

48. Zheng Q, Sharf A, Wan G, Li Y, Mitra N J, Cohen-Or D, Chen B. Non-local scan consolidation for 3D urban scenes. 2010, 29(4):94:91–94:99 DOI:10.1145/1778765.1778831

49. Friedman S, Stamos I. Online facade reconstruction from dominant frequencies in structured point clouds. In: 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops. Providence, RI, USA, IEEE, 2012,1–8 DOI:10.1109/cvprw.2012.6238908

50. Pauly M, Mitra N J, Wallner J, Pottmann H, Guibas L J. Discovering structural regularity in 3D geometry. In: ACM SIGGRAPH 2008 papers on-SIGGRAPH '08. Los Angeles, California, New York, USA, ACM Press, 2008, 1–11 DOI:10.1145/1399504.1360642

51. Li Y Y, Dai A, Guibas L, Nießner M. Database-assisted object retrieval for real-time 3D reconstruction. Computer Graphics Forum, 2015, 34(2): 435–446 DOI:10.1111/cgf.12573

52. Pauly M, Mitra N J, Giesen J, Gross M, Guibas L J. Example-based 3D scan completion. In: Proceedings of the third Eurographics symposium on Geometry processing. Vienna, Austria, Eurographics Association, 2005, 23

53. Nan L L, Xie K, Sharf A. A search-classify approach for cluttered indoor scene understanding. ACM Transactions on Graphics, 2012, 31(6): 1–10 DOI:10.1145/2366145.2366156

54. Kalogerakis E, Chaudhuri S, Koller D, Koltun V. A probabilistic model for component-based shape synthesis. ACM Transactions on Graphics, 2012, 31(4): 1–11 DOI:10.1145/2185520.2185551

55. Girdhar R, Fouhey D F, Rodriguez M, Gupta A. Learning a predictable and generative vector representation for objects//Computer Vision–ECCV 2016. Cham: Springer International Publishing, 2016, 484–499 DOI:10.1007/978-3-319-46466-4_29

57. Guan H Y, Li J, Yu Y T, Chapman M, Wang H Y, Wang C, Zhai R F. Iterative tensor voting for pavement crack extraction using mobile laser scanning data. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(3): 1527–1537 DOI:10.1109/tgrs.2014.2344714

58. Lin Y B, Wang C, Cheng J, Chen B L, Jia F K, Chen Z G, Li J. Line segment extraction for large scale unorganized point clouds. ISPRS Journal of Photogrammetry and Remote Sensing, 2015, 102: 172–183 DOI:10.1016/j.isprsjprs.2014.12.027

59. Zheng G, Moskal L M, Kim S H. Retrieval of effective leaf area index in heterogeneous forests with terrestrial laser scanning. IEEE Transactions on Geoscience and Remote Sensing, 2013, 51(2): 777–786 DOI:10.1109/tgrs.2012.2205003

60. Wang Z, Zhang L Q, Fang T, Mathiopoulos P T, Tong X H, Qu H M, Xiao Z Q, Li F, Chen D. A multiscale and hierarchical feature extraction method for terrestrial laser scanning point cloud classification. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(5): 2409–2425 DOI:10.1109/tgrs.2014.2359951

61. Pathak K, Birk A, Vaškevičius N, Poppinga J. Fast registration based on noisy planes with unknown correspondences for 3-D mapping. IEEE Transactions on Robotics, 2010, 26(3): 424–441 DOI:10.1109/tro.2010.2042989

62. von Gioi R G, Jakubowicz J, Morel J M, Randall G. LSD: a fast line segment detector with a false detection control. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 32(4): 722–732 DOI:10.1109/tpami.2008.300

63. Akinlar C, Topal C. EDLines: a real-time line segment detector with a false detection control. Pattern Recognition Letters, 2011, 32(13): 1633–1642 DOI:10.1016/j.patrec.2011.06.001

64. Jain A, Kurz C, Thormahlen T, Seidel H P. Exploiting global connectivity constraints for reconstruction of 3D line segments from images. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Francisco, CA, USA, IEEE, 2010, 1586–1593 DOI:10.1109/cvpr.2010.5539781

65. Daniels J, Ha L K, Ochotta T, Silva C T. Robust smooth feature extraction from point clouds. In: IEEE International Conference on Shape Modeling and Applications 2007 (SMI '07). Minneapolis, MN, USA, IEEE, 2007,123–136 DOI:10.1109/smi.2007.32

66. Kim S K. Extraction of ridge and valley lines from unorganized points. Multimedia Tools and Applications, 2013, 63(1): 265–279 DOI:10.1007/s11042-012-0999-y

67. Lin Y B, Wang C, Chen B L, Zai D W, Li J. Facet segmentation-based line segment extraction for large-scale point clouds. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(9): 4839–4854 DOI:10.1109/tgrs.2016.2639025

68. Besl P J, Jain R C. Segmentation through variable-order surface fitting. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1988, 10(2): 167–192 DOI:10.1109/34.3881

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

70. Masuta H, Makino S, Lim H O. 3D plane detection for robot perception applying particle swarm optimization. In: 2014 World Automation Congress (WAC). Waikoloa, HI, IEEE, 2014, 549–554 DOI:10.1109/wac.2014.6936041

71. Duda R O, Hart P E. Use of the Hough transformation to detect lines and curves in pictures. Communications of the ACM, 1972, 15(1): 11–15 DOI:10.1145/361237.361242

72. Xu L, Oja E, Kultanen P. A new curve detection method: Randomized Hough transform (RHT). Pattern Recognition Letters, 1990, 11(5): 331–338 DOI:10.1016/0167-8655(90)90042-z

73. Fischler M A, Bolles R C. Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM, 1981, 24(6): 381–395 DOI:10.1145/358669.358692

74. Awwad T M, Zhu Q, Du Z Q, Zhang Y T. An improved segmentation approach for planar surfaces from unstructured 3D point clouds. The Photogrammetric Record, 2010, 25(129): 5–23 DOI:10.1111/j.1477-9730.2009.00564.x

75. Schnabel R, Wahl R, Klein R. Efficient RANSAC for point-cloud shape detection. Computer Graphics Forum, 2007, 26(2): 214–226 DOI:10.1111/j.1467-8659.2007.01016.x

76. Lin Y, Li J, Wang C, Chen Z, Wang Z, Li J. Fast Regularity-Constrained Plane Reconstruction. 2019

77. El-Sayed E, Abdel-Kader R F, Nashaat H, Marei M. Plane detection in 3D point cloud using octree-balanced density down-sampling and iterative adaptive plane extraction. IET Image Processing, 2018, 12(9): 1595–1605 DOI:10.1049/iet-ipr.2017.1076

78. Nguyen H L, Belton D, Helmholz P. Planar surface detection for sparse and heterogeneous mobile laser scanning point clouds. ISPRS Journal of Photogrammetry and Remote Sensing, 2019, 151: 141–161 DOI:10.1016/j.isprsjprs.2019.03.006

79. Kwon H, Kim M, Lee J, Kim J, Doh N L, You B J. Robust plane extraction using supplementary expansion for low-density point cloud data. In: 2018 15th International Conference on Ubiquitous Robots (UR). Honolulu, HI, IEEE, 2018, 501–505 DOI:10.1109/urai.2018.8441776

80. Papon J, Abramov A, Schoeler M, Worgotter F. Voxel cloud connectivity segmentation-supervoxels for point clouds. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition. Portland, OR, USA, IEEE, 2013, 2027–2034 DOI:10.1109/cvpr.2013.264

81. Babahajiani P, Fan L X, Kamarainen J, Gabbouj M. Automated super-voxel based features classification of urban environments by integrating 3D point cloud and image content. In: 2015 IEEE International Conference on Signal and Image Processing Applications (ICSIPA). Kuala Lumpur, Malaysia, IEEE, 2015, 372–377 DOI:10.1109/icsipa.2015.7412219

82. Lin Y B, Wang C, Zhai D W, Li W, Li J. Toward better boundary preserved supervoxel segmentation for 3D point clouds. ISPRS Journal of Photogrammetry and Remote Sensing, 2018, 143: 39–47 DOI:10.1016/j.isprsjprs.2018.05.004

83. Zai D W, Li J, Guo Y L, Cheng M, Lin Y B, Luo H, Wang C. 3-D road boundary extraction from mobile laser scanning data via supervoxels and graph cuts. IEEE Transactions on Intelligent Transportation Systems, 2018, 19(3): 802–813 DOI:10.1109/tits.2017.2701403

84. Wang H Y, Wang C, Luo H, Li P, Chen Y P, Li J. 3-D point cloud object detection based on supervoxel neighborhood with hough forest framework. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015, 8(4): 1570–1581 DOI:10.1109/jstars.2015.2394803

85. Besl P J, McKay N D. A method for registration of 3-D shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1992, 14(2): 239–256 DOI:10.1109/34.121791

86. Bae K H, Lichti D D. A method for automated registration of unorganised point clouds. ISPRS Journal of Photogrammetry and Remote Sensing, 2008, 63(1): 36–54 DOI:10.1016/j.isprsjprs.2007.05.012

87. Gressin A, Mallet C, Demantké J, David N. Towards 3D lidar point cloud registration improvement using optimal neighborhood knowledge. ISPRS Journal of Photogrammetry and Remote Sensing, 2013, 79: 240–251 DOI:10.1016/j.isprsjprs.2013.02.019

88. Stechschulte J, Heckman C. Hidden Markov random field iterative closest point. 2017

89. Yang J L, Li H D, Campbell D, Jia Y D. Go-ICP: a globally optimal solution to 3D ICP point-set registration. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016, 38(11): 2241–2254 DOI:10.1109/tpami.2015.2513405

90. Campbell D, Petersson L. GOGMA: globally-optimal Gaussian mixture alignment. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, NV, USA, IEEE, 2016, 5685–5694 DOI:10.1109/cvpr.2016.613

91. Straub J, Campbell T, How J P, Fisher J W. Efficient global point cloud alignment using Bayesian nonparametric mixtures. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu, HI, IEEE, 2017, 2403–2412 DOI:10.1109/cvpr.2017.258

92. Tombari F, Salti S, di Stefano L. Unique signatures of histograms for local surface description//Computer Vision–ECCV 2010. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010, 356–369 DOI:10.1007/978-3-642-15558-1_26

93. Guo Y L, Sohel F, Bennamoun M, Lu M, Wan J W. Rotational projection statistics for 3D local surface description and object recognition. International Journal of Computer Vision, 2013, 105(1): 63–86 DOI:10.1007/s11263-013-0627-y

94. Yang J Q, Zhang Q, Xiao Y, Cao Z G. TOLDI: an effective and robust approach for 3D local shape description. Pattern Recognition, 2017, 65: 175–187 DOI:10.1016/j.patcog.2016.11.019

95. Rusu R B, Blodow N, Marton Z C, Beetz M. Aligning point cloud views using persistent feature histograms. In: 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems. Nice, IEEE, 2008, 3384–3391 DOI:10.1109/iros.2008.4650967

96. Zai D W, Li J, Guo Y L, Cheng M, Huang P D, Cao X F, Wang C. Pairwise registration of TLS point clouds using covariance descriptors and a non-cooperative game. ISPRS Journal of Photogrammetry and Remote Sensing, 2017, 134,15–29 DOI:10.1016/j.isprsjprs.2017.10.001

97. Zeng A, Song S R, Niessner M, Fisher M, Xiao J X, Funkhouser T. 3DMatch: learning local geometric descriptors from RGB-D reconstructions. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu, HI, IEEE, 2017,199–208 DOI:10.1109/cvpr.2017.29

98. Huang H B, Kalogerakis E, Chaudhuri S, Ceylan D, Kim V G, Yumer E. Learning local shape descriptors from part correspondences with multiview convolutional networks. ACM Transactions on Graphics, 2018, 37(1): 1–14 DOI:10.1145/3137609

99. Elbaz G, Avraham T, Fischer A. 3D point cloud registration for localization using a deep neural network auto-encoder. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu, HI, IEEE, 2017, 2472–2481 DOI:10.1109/cvpr.2017.265

100. Khoury M, Zhou Q-Y, Koltun V. Learning compact geometric features. In: Proceedings of the IEEE International Conference on Computer Vision. 2017, 153–161

101. Deng H W, Birdal T, Ilic S. PPF-FoldNet: unsupervised learning of rotation invariant 3D local descriptors//Computer Vision–ECCV 2018. Cham: Springer International Publishing, 2018, 620–638 DOI:10.1007/978-3-030-01228-1_37

102. Gojcic Z, Zhou C F, Wegner J D, Wieser A. The perfect match: 3D point cloud matching with smoothed densities. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach, CA, USA, IEEE, 2019, 5545–5554 DOI:10.1109/cvpr.2019.00569

103. Xu Y S, Boerner R, Yao W, Hoegner L, Stilla U. Pairwise coarse registration of point clouds in urban scenes using voxel-based 4-planes congruent sets. ISPRS Journal of Photogrammetry and Remote Sensing, 2019, 151: 106–123 DOI:10.1016/j.isprsjprs.2019.02.015

104. Shi X J, Liu T, Han X. Improved Iterative Closest Point (ICP) 3D point cloud registration algorithm based on point cloud filtering and adaptive fireworks for coarse registration. International Journal of Remote Sensing, 2020, 41(8): 3197–3220 DOI:10.1080/01431161.2019.1701211

105. Deng H W, Birdal T, Ilic S. PPFNet: global context aware local features for robust 3D point matching. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, UT, IEEE, 2018, 195–205 DOI:10.1109/cvpr.2018.00028

106. Georgakis G, Karanam S, Wu Z Y, Ernst J, Kosecka J. End-to-end learning of keypoint detector and descriptor for pose invariant 3D matching. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, UT, IEEE, 2018, 1965–1973 DOI:10.1109/cvpr.2018.00210

107. Yew Z J, Lee G H. 3DFeat-net: weakly supervised local 3D features for point cloud registration//Computer Vision–ECCV 2018. Cham: Springer International Publishing, 2018, 630–646 DOI:10.1007/978-3-030-01267-0_37

108. Deng H W, Birdal T, Ilic S. 3D local features for direct pairwise registration. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach, CA, USA, IEEE, 2019, 3244–3253 DOI:10.1109/cvpr.2019.00336

109. Aoki Y, Goforth H, Srivatsan R A, Lucey S. PointNetLK: robust & efficient point cloud registration using PointNet. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach, CA, USA, IEEE, 2019, 7163–7172 DOI:10.1109/cvpr.2019.00733

110. Sarode V, Li X Q, Goforth H, Aoki Y, Choset H. PCRNet: point cloud registration network using PointNet encoding. 2019

111. Aiger D, Mitra N J, Cohen-Or D. 4-points congruent sets for robust pairwise surface registration. In: ACM SIGGRAPH 2008 papers on-SIGGRAPH '08. Los Angeles, California, New York, USA, ACM Press, 2008, 1–10 DOI:10.1145/1399504.1360684

112. Mellado N, Aiger D, Mitra N J. Super 4PCS fast global pointcloud registration via smart indexing. Computer Graphics Forum, 2014, 33(5): 205–215 DOI:10.1111/cgf.12446

113. Che E Z, Jung J, Olsen M. Object recognition, segmentation, and classification of mobile laser scanning point clouds: a state of the art review. Sensors, 2019, 19(4): 810 DOI:10.3390/s19040810

114. Hackel T, Wegner J D, Schindler K. Fast semantic segmentation of 3d point clouds with strongly varying density. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, 2016, III-3: 177–184 DOI:10.5194/isprs-annals-iii-3-177-2016

115. Weinmann M, Jutzi B, Mallet C. Semantic 3D scene interpretation: a framework combining optimal neighborhood size selection with relevant features. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, 2014, II-3: 181–188 DOI:10.5194/isprsannals-ii-3-181-2014

116. Hu H Z, Munoz D, Bagnell J A, Hebert M. Efficient 3-D scene analysis from streaming data. In: 2013 IEEE International Conference on Robotics and Automation. Karlsruhe, Germany, IEEE, 2013, 2297–2304 DOI:10.1109/icra.2013.6630888

117. Zhao H J, Liu Y M, Zhu X L, Zhao Y P, Zha H B. Scene understanding in a large dynamic environment through a laser-based sensing. In: 2010 IEEE International Conference on Robotics and Automation. Anchorage, AK, IEEE, 2010, 127–133 DOI:10.1109/robot.2010.5509169

118. Lu Y, Rasmussen C. Simplified Markov random fields for efficient semantic labeling of 3D point clouds. In: 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems. Vilamoura-Algarve, Portugal, IEEE, 2012, 2690–2697 DOI:10.1109/iros.2012.6386039

119. Munoz D, Bagnell J A, Vandapel N, Hebert M. Contextual classification with functional max-margin Markov networks. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition. Miami, FL, IEEE, 2009, 975–982 DOI:10.1109/cvpr.2009.5206590

120. Wu Z R, Song S R, Khosla A, Yu F, Zhang L G, Tang X O, Xiao J X. 3D ShapeNets: a deep representation for volumetric shapes. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition. Boston, MA, USA, IEEE, 2015, 1912–1920 DOI:10.1109/cvpr.2015.7298801

121. Qi C R, Su H, NieBner M, Dai A, Yan M Y, Guibas L J. Volumetric and multi-view CNNs for object classification on 3D data. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, NV, USA, IEEE, 2016, 5648–5656 DOI:10.1109/cvpr.2016.609

122. Maturana D, Scherer S. VoxNet: a 3D Convolutional Neural Network for real-time object recognition. In: 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Hamburg, Germany, IEEE, 2015, 922–928 DOI:10.1109/iros.2015.7353481

123. Su H, Maji S, Kalogerakis E, Learned-Miller E. Multi-view convolutional neural networks for 3D shape recognition. In: 2015 IEEE International Conference on Computer Vision. Santiago, Chile, IEEE, 2015, 945–953 DOI:10.1109/iccv.2015.114

124. Leng B, Guo S, Zhang X Y, Xiong Z. 3D object retrieval with stacked local convolutional autoencoder. Signal Processing, 2015, 112: 119–128 DOI:10.1016/j.sigpro.2014.09.005

125. Tosteberg P. Semantic segmentation of point clouds using deep learning. 2017

126. Wu B C, Wan A, Yue X Y, Keutzer K. SqueezeSeg: convolutional neural nets with recurrent CRF for real-time road-object segmentation from 3D LiDAR point cloud. In: 2018 IEEE International Conference on Robotics and Automation (ICRA). Brisbane, QLD, IEEE, 2018, 1887–1893 DOI:10.1109/icra.2018.8462926

127. Piewak F, Pinggera P, Schäfer M, Peter D, Schwarz B, Schneider N, Enzweiler M, Pfeiffer D, Zöllner M. Boosting LiDAR-based semantic labeling by cross-modal training data generation//Lecture Notes in Computer Science. Cham: Springer International Publishing, 2019, 497–513 DOI:10.1007/978-3-030-11024-6_39

128. Caltagirone L, Scheidegger S, Svensson L, Wahde M. Fast LIDAR-based road detection using fully convolutional neural networks. In: 2017 IEEE Intelligent Vehicles Symposium (IV). Los Angeles, CA, USA, IEEE, 2017, 1019–1024 DOI:10.1109/ivs.2017.7995848

129. Lawin F J, Danelljan M, Tosteberg P, Bhat G, Khan F S, Felsberg M. Deep projective 3D semantic segmentation//Computer Analysis of Images and Patterns. Cham: Springer International Publishing, 2017, 95–107 DOI:10.1007/978-3-319-64689-3_8

130. Wang X, Chan T O, Liu K, Pan J, Luo M, Li W, Wei C. A robust segmentation framework for closely packed buildings from airborne LiDAR point clouds. International Journal of Remote Sensing, 2020, 41(14): 5147–5165 DOI:10.1080/01431161.2020.1727053

131. Guo Z, Feng C C. Using multi-scale and hierarchical deep convolutional features for 3D semantic classification of TLS point clouds. International Journal of Geographical Information Science, 2020, 34(4): 661–680 DOI:10.1080/13658816.2018.1552790

132. Charles R Q, Hao S, Mo K C, Guibas L J. PointNet: deep learning on point sets for 3D classification and segmentation. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, HI, IEEE, 2017, 652–660 DOI:10.1109/cvpr.2017.16

133. Qi C R, Yi L, Su H, Guibas L J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. In: Advances in Neural Information Processing Systems. 2017, 5099–5108

134. Zhou Y, Tuzel O. VoxelNet: end-to-end learning for point cloud based 3D object detection. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, UT, USA, IEEE, 2018, 4490–4499 DOI:10.1109/cvpr.2018.00472

135. Li Y, Bu R, Sun M, Wu W, Di X, Chen B. Pointcnn: Convolution on x-transformed points. In: Advances in Neural Information Processing Systems. 2018, 820–830

136. Wang Y, Sun Y B, Liu Z W, Sarma S E, Bronstein M M, Solomon J M. Dynamic graph CNN for learning on point clouds. ACM Transactions on Graphics, 2019, 38(5): 1–12 DOI:10.1145/3326362

137. Huang R, Hong D F, Xu Y S, Yao W, Stilla U. Multi-scale local context embedding for LiDAR point cloud classification. IEEE Geoscience and Remote Sensing Letters, 2020, 17(4): 721–725 DOI:10.1109/lgrs.2019.2927779

138. Tchapmi L, Choy C, Armeni I, Gwak J, Savarese S. SEGCloud: semantic segmentation of 3D point clouds. In: 2017 International Conference on 3D Vision (3DV). Qingdao, IEEE, 2017, 537–547 DOI:10.1109/3dv.2017.00067

139. Hackel T, Savinov N, Ladicky L, Wegner J D, Schindler K, Pollefeys M. Semantic3d.net: a new large-scale point cloud classification benchmark. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, 2017, IV-1/W1: 91–98 DOI:10.5194/isprs-annals-iv-1-w1-91-2017

140. Riegler G, Ulusoy A O, Geiger A. OctNet: learning deep 3D representations at high resolutions. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 2017, 3577–3586 DOI:10.1109/cvpr.2017.701

141. Riemenschneider H, Bódis-Szomorú A, Weissenberg J, van Gool L. Learning where to classify in multi-view semantic segmentation//Computer Vision–ECCV 2014. Cham: Springer International Publishing, 2014, 516–532 DOI:10.1007/978-3-319-10602-1_34

142. Engelmann F, Kontogianni T, Hermans A, Leibe B. Exploring spatial context for 3D semantic segmentation of point clouds. In: 2017 IEEE International Conference on Computer Vision Workshops. Venice, IEEE, 2017, 716–724 DOI:10.1109/iccvw.2017.90

143. Landrieu L, Simonovsky M. Large-scale point cloud semantic segmentation with superpoint graphs. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, UT, IEEE, 2018, 4558–4567 DOI:10.1109/cvpr.2018.00479

144. Xu Y S, Ye Z, Yao W, Huang R, Tong X H, Hoegner L, Stilla U. Classification of LiDAR point clouds using supervoxel-based detrended feature and perception-weighted graphical model. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020, 13: 72–88 DOI:10.1109/jstars.2019.2951293

145. Bronstein M M, Bruna J, LeCun Y, Szlam A, Vandergheynst P. Geometric deep learning: going beyond euclidean data. IEEE Signal Processing Magazine, 2017, 34(4): 18–42 DOI:10.1109/msp.2017.2693418

146. Premebida C, Carreira J, Batista J, Nunes U. Pedestrian detection combining RGB and dense LIDAR data. In: 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems. Chicago, IL, USA, IEEE, 2014, 4112–4117 DOI:10.1109/iros.2014.6943141

147. Gonzalez A, Villalonga G, Xu J L, Vazquez D, Amores J, Lopez A M. Multiview random forest of local experts combining RGB and LIDAR data for pedestrian detection. In: 2015 IEEE Intelligent Vehicles Symposium (IV). Seoul, South Korea, IEEE, 2015, 356–361 DOI:10.1109/ivs.2015.7225711

148. Li B, Zhang T, Xia T. Vehicle detection from 3d lidar using fully convolutional network. 2016 DOI:10.15607/rss.2016.xii.042

149. Chen X, Kundu K, Zhu Y, Berneshawi A G, Ma H, Fidler S, Urtasun R. 3D object proposals for accurate object class detection. In: Advances in Neural Information Processing Systems. 2015, 424–432

150. Yang B, Luo W J, Urtasun R. PIXOR: real-time 3D object detection from point clouds. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, UT, USA, IEEE, 2018, 7652–7660 DOI:10.1109/cvpr.2018.00798

151. Zeng Wang D, Posner I. Voting for voting in online point cloud object detection. In: Robotics: Science and Systems XI, Robotics: Science and Systems Foundation, 2015: 1(3):10–15607 DOI:10.15607/rss.2015.xi.035

152. Engelcke M, Rao D, Wang D Z, Tong C H, Posner I. Vote3Deep: Fast object detection in 3D point clouds using efficient convolutional neural networks. In: 2017 IEEE International Conference on Robotics and Automation (ICRA). Singapore, IEEE, 2017, 1355–1361 DOI:10.1109/icra.2017.7989161

153. Chen X Z, Ma H M, Wan J, Li B, Xia T. Multi-view 3D object detection network for autonomous driving. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, HI, IEEE, 2017, 1907–1915 DOI:10.1109/cvpr.2017.691

154. Li B. 3D fully convolutional network for vehicle detection in point cloud. In: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Vancouver, BC, IEEE, 2017, 1513–1518 DOI:10.1109/iros.2017.8205955

155. Maturana D, Scherer S. 3D Convolutional Neural Networks for landing zone detection from LiDAR. In: 2015 IEEE International Conference on Robotics and Automation (ICRA). Seattle, WA, USA, IEEE, 2015, 3471–3478 DOI:10.1109/icra.2015.7139679

156. Shi S S, Wang X G, Li H S. PointRCNN: 3D object proposal generation and detection from point cloud. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach, CA, USA, IEEE, 2019, 770–779 DOI:10.1109/cvpr.2019.00086

157. Qi C R, Litany O, He K M, Guibas L. Deep hough voting for 3D object detection in point clouds. In: 2019 IEEE/CVF International Conference on Computer Vision (ICCV). Seoul, Korea (South), IEEE, 2019, 9277–9286 DOI:10.1109/iccv.2019.00937

158. Lang A H, Vora S, Caesar H, Zhou L B, Yang J, Beijbom O. PointPillars: fast encoders for object detection from point clouds. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach, CA, USA, IEEE, 2019, 12697–12705 DOI:10.1109/cvpr.2019.01298

159. Yi C, Zhang Y, Wu Q Y, Xu Y B, Remil O, Wei M Q, Wang J. Urban building reconstruction from raw LiDAR point data. Computer-Aided Design, 2017, 93: 1–14 DOI:10.1016/j.cad.2017.07.005

160. Zhou Z X, Gong J. Automated analysis of mobile LiDAR data for component-level damage assessment of building structures during large coastal storm events. Computer-Aided Civil and Infrastructure Engineering, 2018, 33(5): 373–392 DOI:10.1111/mice.12345

161. Goebbels S, Pohle-Fröhlich R. Quality enhancement techniques for building models derived from sparse point clouds. In: Proceedings of the 12th International Joint Conference on Computer Vision. Imaging and Computer Graphics Theory and Applications, 2017, 93–104 DOI:10.5220/0006103300930104

162. Zhang D, Du P. 3D building reconstruction from lidar data based on Delaunay TIN approach. SPIE, 2011

163. Chen L C, Teo T A, Kuo C Y, Rau J Y. Shaping polyhedral buildings by the fusion of vector maps and lidar point clouds. Photogrammetric Engineering & Remote Sensing, 2007, 73(9): 1147–1157 DOI:10.14358/pers.73.9.1147

164. Xiong B, Jancosek M, Oude Elberink S, Vosselman G. Flexible building primitives for 3D building modeling. ISPRS Journal of Photogrammetry and Remote Sensing, 2015, 101: 275–290 DOI:10.1016/j.isprsjprs.2015.01.002

165. Wang Y Z, Ma Y Q, Zhu A X, Zhao H, Liao L X. Accurate facade feature extraction method for buildings from three-dimensional point cloud data considering structural information. ISPRS Journal of Photogrammetry and Remote Sensing, 2018, 139: 146–153 DOI:10.1016/j.isprsjprs.2017.11.015

166. Zhang L Q, Li Z Q, Li A J, Liu F Y. Large-scale urban point cloud labeling and reconstruction. ISPRS Journal of Photogrammetry and Remote Sensing, 2018, 138: 86–100 DOI:10.1016/j.isprsjprs.2018.02.008

167. Díaz-Vilariño L, Khoshelham K, Martínez-Sánchez J, Arias P. 3D modeling of building indoor spaces and closed doors from imagery and point clouds. Sensors, 2015, 15(2): 3491–3512 DOI:10.3390/s150203491

168. Stambler A, Huber D. Building modeling through enclosure reasoning. In: 2014 2nd International Conference on 3D Vision. Tokyo, IEEE, 2014, 118–125 DOI:10.1109/3dv.2014.65

169. Javanmardi M, Gu Y L, Javanmardi E, Hsu L T, Kamijo S. 3D building map reconstruction in dense urban areas by integrating airborne laser point cloud with 2D boundary map. In: 2015 IEEE International Conference on Vehicular Electronics and Safety (ICVES). Yokohama, Japan, IEEE, 2015, 126–131 DOI:10.1109/icves.2015.7396906

170. Zhang L Q, Zhang L. Deep learning-based classification and reconstruction of residential scenes from large-scale point clouds. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(4): 1887–1897 DOI:10.1109/tgrs.2017.2769120

171. López F J, Lerones P M, Llamas J, Gómez-García-bermejo J, Zalama E. A framework for using point cloud data of heritage buildings toward geometry modeling in A BIM context: a case study on santa maria La real de mave church. International Journal of Architectural Heritage, 2017, 1–22 DOI:10.1080/15583058.2017.1325541

172. Ochmann S, Vock R, Wessel R, Klein R. Automatic reconstruction of parametric building models from indoor point clouds. Computers & Graphics, 2016, 54: 94–103 DOI:10.1016/j.cag.2015.07.008

173. Xiong B, Elberink S O, Vosselman G. Building modeling from noisy photogrammetric point clouds. 2014, 2(3):197

174. Hojebri B, Samadzadegan F, Arefi H. Building reconstruction based on the data fusion of lidar point cloud and aerial imagery. 2014, 103–121

175. Hron V, Halounová L. Automatic generation of 3D building models from point clouds//Lecture Notes in Geoinformation and Cartography. Cham: Springer International Publishing, 2014, 109–119 DOI:10.1007/978-3-319-11463-7_8

176. Chen Y C, Lin B Y, Lin C H. Consistent roof geometry encoding for 3D building model retrieval using airborne LiDAR point clouds. ISPRS International Journal of Geo-Information, 2017, 6(9): 269 DOI:10.3390/ijgi6090269

177. Zhang Y J, Li X H, Wang Q, Liu J, Liang X, Li D, Ni C D, Liu Y. LIDAR point cloud data extraction and establishment of 3D modeling of buildings. IOP Conference Series: Materials Science and Engineering, 2018, 301: 012037 DOI:10.1088/1757-899x/301/1/012037

178. Chen J Y, Lin C H, Hsu P C, Chen C H. Point cloud encoding for 3D building model retrieval. IEEE Transactions on Multimedia, 2014, 16(2): 337–345 DOI:10.1109/tmm.2013.2286580

179. Demir I, Aliaga D G, Benes B. Procedural editing of 3D building point clouds. In: 2015 IEEE International Conference on Computer Vision. Santiago, IEEE, 2015, 2147–2155 DOI:10.1109/iccv.2015.248

180. Wu T, Hu X Y, Ye L Z. Fast and accurate plane segmentation of airborne LiDAR point cloud using cross-line elements. Remote Sensing, 2016, 8(5): 383 DOI:10.3390/rs8050383

181. Wang C, Hou S W, Wen C L, Gong Z, Li Q, Sun X T, Li J. Semantic line framework-based indoor building modeling using backpacked laser scanning point cloud. ISPRS Journal of Photogrammetry and Remote Sensing, 2018, 143: 150–166 DOI:10.1016/j.isprsjprs.2018.03.025

182. Chen D, Zhang L Q, Mathiopoulos P T, Huang X F. A methodology for automated segmentation and reconstruction of urban 3-D buildings from ALS point clouds. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(10): 4199–4217 DOI:10.1109/jstars.2014.2349003

183. Seif H G, Hu X L. Autonomous driving in the iCity: HD maps as a key challenge of the automotive industry. Engineering, 2016, 2(2): 159–162 DOI:10.1016/j.eng.2016.02.010

184. Bauer S, Alkhorshid Y, Wanielik G. Using High-Definition maps for precise urban vehicle localization. In: 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC). Rio de Janeiro, Brazil, IEEE, 2016, 492–497 DOI:10.1109/itsc.2016.7795600

185. Zeng W Y, Luo W J, Suo S, Sadat A, Yang B, Casas S, Urtasun R. End-to-end interpretable neural motion planner. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach, CA, USA, IEEE, 2019, 8660–8669 DOI:10.1109/cvpr.2019.00886

186. Zhang R, Chen C, Di Z, Wheeler M D. Visual odometry and pairwise alignment for high definition map creation. 2019

187. Siam M, Elkerdawy S, Jagersand M, Yogamani S. Deep semantic segmentation for automated driving: Taxonomy, roadmap and challenges. In: 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC). Yokohama, IEEE, 2017, 1–8 DOI:10.1109/itsc.2017.8317714

188. Barsi A, Poto V, Somogyi A, Lovas T, Tihanyi V, Szalay Z. Supporting autonomous vehicles by creating HD maps. Production Engineering Archives, 2017, 16: 43–46 DOI:10.30657/pea.2017.16.09

189. Ma L F, Li Y, Li J, Wang C, Wang R S, Chapman M. Mobile laser scanned point-clouds for road object detection and extraction: a review. Remote Sensing, 2018, 10(10): 1531 DOI:10.3390/rs10101531

190. Wu F, Wen C L, Guo Y L, Wang J J, Yu Y T, Wang C, Li J. Rapid localization and extraction of street light poles in mobile LiDAR point clouds: a supervoxel-based approach. IEEE Transactions on Intelligent Transportation Systems, 2017, 18(2): 292–305 DOI:10.1109/tits.2016.2565698

191. Hata A Y, Osorio F S, Wolf D F. Robust curb detection and vehicle localization in urban environments. In: 2014 IEEE Intelligent Vehicles Symposium Proceedings. MI, USA, IEEE, 2014, 1257–1262 DOI:10.1109/ivs.2014.6856405

192. Guan H Y, Li J, Yu Y T, Wang C, Chapman M, Yang B S. Using mobile laser scanning data for automated extraction of road markings. ISPRS Journal of Photogrammetry and Remote Sensing, 2014, 87: 93–107 DOI:10.1016/j.isprsjprs.2013.11.005

193. Riveiro B, González-Jorge H, Martínez-Sánchez J, Díaz-Vilariño L, Arias P. Automatic detection of zebra crossings from mobile LiDAR data. Optics & Laser Technology, 2015, 70: 63–70 DOI:10.1016/j.optlastec.2015.01.011

194. Yang B S, Dong Z, Liu Y, Liang F X, Wang Y J. Computing multiple aggregation levels and contextual features for road facilities recognition using mobile laser scanning data. ISPRS Journal of Photogrammetry and Remote Sensing, 2017, 126: 180–194 DOI:10.1016/j.isprsjprs.2017.02.014

195. Wang H Y, Luo H, Wen C L, Cheng J, Li P, Chen Y P, Wang C, Li J. Road boundaries detection based on local normal saliency from mobile laser scanning data. IEEE Geoscience and Remote Sensing Letters, 2015, 12(10): 2085–2089 DOI:10.1109/lgrs.2015.2449074

196. Wang H Y, Cai Z P, Luo H, Wang C, Li P, Yang W T, Ren S P, Li J. Automatic road extraction from mobile laser scanning data. In: 2012 International Conference on Computer Vision in Remote Sensing. Xiamen, China, IEEE, 2012, 136–139 DOI:10.1109/cvrs.2012.6421248

197. Rachmadi R F, Uchimura K, Koutaki G, Ogata K. Road edge detection on 3D point cloud data using Encoder-Decoder Convolutional Network. In: 2017 International Electronics Symposium on Knowledge Creation and Intelligent Computing (IES-KCIC). Surabaya, IEEE, 2017, 95–100 DOI:10.1109/kcic.2017.8228570

198. Chen X, Kohlmeyer B, Stroila M, Alwar N, Wang R, Bach J. Next generation map making: geo-referenced ground-level LIDAR point clouds for automatic retro-reflective road feature extraction. In: Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. Seattle, Washington, Association for Computing Machinery, 2009, 488–491 DOI:10.1145/1653771.1653851

199. Yu Y T, Li J, Guan H Y, Jia F K, Wang C. Learning hierarchical features for automated extraction of road markings from 3-D mobile LiDAR point clouds. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015, 8(2): 709–726 DOI:10.1109/jstars.2014.2347276

200. Jung J, Che E Z, Olsen M J, Parrish C. Efficient and robust lane marking extraction from mobile lidar point clouds. ISPRS Journal of Photogrammetry and Remote Sensing, 2019, 147: 1–18 DOI:10.1016/j.isprsjprs.2018.11.012

201. Wen C L, Li J, Luo H, Yu Y T, Cai Z P, Wang H Y, Wang C. Spatial-related traffic sign inspection for inventory purposes using mobile laser scanning data. IEEE Transactions on Intelligent Transportation Systems, 2016, 17(1): 27–37 DOI:10.1109/tits.2015.2418214

202. Arcos-GarcíaÁ, Soilán M, Álvarez-García J A, Riveiro B. Exploiting synergies of mobile mapping sensors and deep learning for traffic sign recognition systems. Expert Systems With Applications, 2017, 89: 286–295 DOI:10.1016/j.eswa.2017.07.042

203. Huang P D, Cheng M, Chen Y P, Luo H, Wang C, Li J. Traffic sign occlusion detection using mobile laser scanning point clouds. IEEE Transactions on Intelligent Transportation Systems, 2017, 18(9): 2364–2376 DOI:10.1109/tits.2016.2639582

204. Yu Y T, Li J, Wen C L, Guan H Y, Luo H, Wang C. Bag-of-visual-phrases and hierarchical deep models for traffic sign detection and recognition in mobile laser scanning data. ISPRS Journal of Photogrammetry and Remote Sensing, 2016, 113: 106–123 DOI:10.1016/j.isprsjprs.2016.01.005

205. Previtali M, Díaz-Vilariño L, Scaioni M. Towards automatic reconstruction of indoor scenes from incomplete point clouds: door and window detection and regularization. ISPRS-International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2018, XLII-4: 507–514 DOI:10.5194/isprs-archives-xlii-4-507-2018

206. Tran H, Khoshelham K, Kealy A, Díaz-Vilariño L. Shape grammar approach to 3D modeling of indoor environments using point clouds. Journal of Computing in Civil Engineering, 2019, 33(1): 04018055 DOI:10.1061/(asce)cp.1943-5487.0000800

207. Shi W Z, Ahmed W, Li N, Fan W Z, Xiang H D, Wang M Y. Semantic geometric modelling of unstructured indoor point cloud. ISPRS International Journal of Geo-Information, 2018, 8(1): 9 DOI:10.3390/ijgi8010009

208. Xiao Y, Taguchi Y, Kamat V R. Coupling point cloud completion and surface connectivity relation inference for 3D modeling of indoor building environments. Journal of Computing in Civil Engineering, 2018, 32(5): 04018033 DOI:10.1061/(asce)cp.1943-5487.0000776

209. Díaz-Vilariño L, Verbree E, Zlatanova S, Diakité A. Indoor modelling from slam-based laser scanner: door detection to envelope reconstruction. ISPRS-International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2017, XLII-2/W7: 345–352 DOI:10.5194/isprs-archives-xlii-2-w7-345-2017

210. Oesau S, Lafarge F, Alliez P. Indoor scene reconstruction using feature sensitive primitive extraction and graph-cut. ISPRS Journal of Photogrammetry and Remote Sensing, 2014, 90: 68–82 DOI:10.1016/j.isprsjprs.2014.02.004

211. Ochmann S, Vock R, Klein R. Automatic reconstruction of fully volumetric 3D building models from oriented point clouds. ISPRS Journal of Photogrammetry and Remote Sensing, 2019, 151: 251–262 DOI:10.1016/j.isprsjprs.2019.03.017

212. Li L, Su F, Yang F, Zhu H H, Li D L, Zuo X K, Li F, Liu Y, Ying S. Reconstruction of three-dimensional (3D) indoor interiors with multiple stories via comprehensive segmentation. Remote Sensing, 2018, 10(8): 1281 DOI:10.3390/rs10081281

213. Sanchez V, Zakhor A. Planar 3D modeling of building interiors from point cloud data. In: 2012 19th IEEE International Conference on Image Processing. Orlando, FL, USA, IEEE, 2012,1777–1780 DOI:10.1109/icip.2012.6467225

214. Budroni A, Boehm J. Automated 3D reconstruction of interiors from point clouds. International Journal of Architectural Computing, 2010, 8(1): 55–73 DOI:10.1260/1478-0771.8.1.55

215. Furukawa Y, Curless B, Seitz S M, Szeliski R. Reconstructing building interiors from images. In: 2009 IEEE 12th International Conference on Computer Vision. Kyoto, IEEE, 2009, 80–87 DOI:10.1109/iccv.2009.5459145

216. Khoshelham K, Díaz-Vilariño L. 3D modelling of interior spaces: learning the language of indoor architecture. ISPRS- International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2014, XL-5: 321–326 DOI:10.5194/isprsarchives-xl-5-321-2014

217. Previtali M, Díaz-Vilariño L, Scaioni M. Indoor building reconstruction from occluded point clouds using graph-cut and ray-tracing. Applied Sciences, 2018, 8(9): 1529 DOI:10.3390/app8091529

218. Kim S, Manduchi R, Qin S Y. Multi-planar monocular reconstruction of Manhattan indoor scenes. In: 2018 International Conference on 3D Vision. Verona, IEEE, 2018, 30–33 DOI:10.1109/3dv.2018.00076

219. Michailidis G T, Pajarola R. Bayesian graph-cut optimization for wall surfaces reconstruction in indoor environments. The Visual Computer, 2017, 33(10): 1347–1355 DOI:10.1007/s00371-016-1230-3

220. Jung J, Stachniss C, Ju S, Heo J. Automated 3D volumetric reconstruction of multiple-room building interiors for as-built BIM. Advanced Engineering Informatics, 2018, 38: 811–825 DOI:10.1016/j.aei.2018.10.007

221. Quintana B, Prieto S A, Adán A, Bosché F. Door detection in 3D coloured point clouds of indoor environments. Automation in Construction, 2018, 85: 146–166 DOI:10.1016/j.autcon.2017.10.016

222. Previtali M, Barazzetti L, Brumana R, Scaioni M. Towards automatic indoor reconstruction of cluttered building rooms from point clouds. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, 2014, 2(5): 281–288 DOI:10.5194/isprsannals-ii-5-281-2014

223. Díaz-Vilariño L, Khoshelham K, Martínez-Sánchez J, Arias P. 3D modeling of building indoor spaces and closed doors from imagery and point clouds. Sensors, 2015, 15(2): 3491–3512 DOI:10.3390/s150203491

224. Díaz-Vilariño L, Boguslawski P, Khoshelham K, Lorenzo H. Obstacle-aware indoor pathfinding using point clouds. ISPRS International Journal of Geo-Information, 2019, 8(5): 233 DOI:10.3390/ijgi8050233

225. Nikoohemat S, Peter M, Oude Elberink S, Vosselman G. Exploiting indoor mobile laser scanner trajectories for semantic interpretation of point clouds. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, 2017, 4: 355–362 DOI:10.5194/isprs-annals-iv-2-w4-355-2017

226. Sun L S, Yao L Y, Rong J, Lu J Y, Liu B H, Wang S W. Simulation analysis on driving behavior during traffic sign recognition. International Journal of Computational Intelligence Systems, 2011, 4(3): 353–360 DOI:10.2991/ijcis.2011.4.3.9

227. Li N X, Busso C. Predicting perceived visual and cognitive distractions of drivers with multimodal features. IEEE Transactions on Intelligent Transportation Systems, 2015, 16(1): 51–65 DOI:10.1109/tits.2014.2324414

228. Lyu N C, Xie L, Wu C Z, Fu Q, Deng C. Driver's cognitive workload and driving performance under traffic sign information exposure in complex environments: a case study of the highways in China. International Journal of Environmental Research and Public Health, 2017, 14(2): 203 DOI:10.3390/ijerph14020203

229. Motamedi A, Wang Z, Yabuki N, Fukuda T, Michikawa T. Signage visibility analysis and optimization system using BIM-enabled virtual reality (VR) environments. Advanced Engineering Informatics, 2017, 32: 248–262 DOI:10.1016/j.aei.2017.03.005

230. Li L D, Zhang Q N. Research on visual cognition about sharp turn sign based on driver's eye movement characteristic. International Journal of Pattern Recognition and Artificial Intelligence, 2017, 31(7): 1759012 DOI:10.1142/s0218001417590121

231. Liu B H, Sun L S, Rong J. Driver's visual cognition behaviors of traffic signs based on eye movement parameters. Journal of Transportation Systems Engineering and Information Technology, 2011, 11(4): 22–27 DOI:10.1016/s1570-6672(10)60129-8

232. Belaroussi R, Gruyer D. Impact of reduced visibility from fog on traffic sign detection. In: 2014 IEEE Intelligent Vehicles Symposium Proceedings. MI, USA, IEEE, 2014, 1302–1306 DOI:10.1109/ivs.2014.6856535

233. Doman K, Deguchi D, Takahashi T, Mekada Y, Ide I, Murase H, Sakai U. Estimation of traffic sign visibility considering local and global features in a driving environment. In: 2014 IEEE Intelligent Vehicles Symposium Proceedings. MI, USA, IEEE, 2014, 202–207 DOI:10.1109/ivs.2014.6856474

234. Doman K, Deguchi D, Takahashi T, Mekada Y, Ide I, Murase H, Tamatsu Y. Estimation of traffic sign visibility toward smart driver assistance. In: 2010 IEEE Intelligent Vehicles Symposium. La Jolla, CA, USA, IEEE, 2010, 45–50 DOI:10.1109/ivs.2010.5548137

235. Doman K, Deguchi D, Takahashi T, Mekada Y, Ide I, Murase H, Tamatsu Y. Estimation of traffic sign visibility considering temporal environmental changes for smart driver assistance. In: 2011 IEEE Intelligent Vehicles Symposium (IV). Baden-Baden, Germany, IEEE, 2011, 667–672 DOI:10.1109/ivs.2011.5940467

236. Balsa-Barreiro J, Valero-Mora P M, Berné-Valero J L, Varela-García F A. GIS mapping of driving behavior based on naturalistic driving data. ISPRS International Journal of Geo-Information, 2019, 8(5): 226 DOI:10.3390/ijgi8050226

237. Balsa-Barreiro J, Sánchez García M, Valero-Mora P M, Pareja Montoro I. Geo-referencing naturalistic driving data using a novel method based on vehicle speed. IET Intelligent Transport Systems, 2013, 7(2): 190–197 DOI:10.1049/iet-its.2012.0152

238. Lee J, Yang J H. Analysis of driver's EEG given take-over alarm in SAE level 3 automated driving in a simulated environment. International Journal of Automotive Technology, 2020, 21(3): 719–728 DOI:10.1007/s12239-020-0070-3

239. Katz S, Tal A, Basri R. Direct visibility of point sets. In: ACM SIGGRAPH 2007 papers on-SIGGRAPH '07. San Diego, California, New York, USA, ACM Press, 2007 DOI:10.1145/1275808.1276407

240. Katz S, Tal A. Improving the visual comprehension of point sets. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition. Portland, OR, USA, IEEE, 2013, 121–128 DOI:10.1109/cvpr.2013.23

241. Zhang S X, Wang C, Lin L L, Wen C L, Yang C H, Zhang Z M, Li J. Automated visual recognizability evaluation of traffic sign based on 3D LiDAR point clouds. Remote Sensing, 2019, 11(12): 1453 DOI:10.3390/rs11121453

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