Visualization of 3D cable between utility poles obtained from laser scanning point clouds: a case study

[1]  P. Mewis Estimation of Vegetation-Induced Flow Resistance for Hydraulic Computations Using Airborne Laser Scanning Data , 2021, Water.

[2]  Werner Lienhart,et al.  Processing of mobile laser scanning data for large‐scale deformation monitoring of anchored retaining structures along highways , 2021, Comput. Aided Civ. Infrastructure Eng..

[3]  A. D. Wulf,et al.  Assessment of handheld mobile terrestrial laser scanning for estimating tree parameters , 2020, Journal of Forestry Research.

[4]  Marco Balsi,et al.  Fully Automatic Point Cloud Analysis for Powerline Corridor Mapping , 2020, IEEE Transactions on Geoscience and Remote Sensing.

[5]  Shoudong Han,et al.  Object spatial localization by fusing 3D point clouds and instance segmentation , 2020 .

[6]  L. Xie,et al.  REAL-TIME POWERLINE CORRIDOR INSPECTION BY EDGE COMPUTING OF UAV LIDAR DATA , 2019, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences.

[7]  Unsang Park,et al.  Robust Powerline Equipment Inspection System Based on a Convolutional Neural Network , 2018, Sensors.

[8]  Takashi Goto,et al.  Highly Accurate and Efficient Maintenance Technology for Optical Cables and Utility Poles , 2018, 2018 Optical Fiber Communications Conference and Exposition (OFC).

[9]  Juha Hyyppä,et al.  Comparison of the Selected State-Of-The-Art 3D Indoor Scanning and Point Cloud Generation Methods , 2017, Remote. Sens..

[10]  Mani Golparvar-Fard,et al.  Visual monitoring of civil infrastructure systems via camera-equipped Unmanned Aerial Vehicles (UAVs): a review of related works , 2016 .

[11]  Qingquan Li,et al.  An Improved Method for Power-Line Reconstruction from Point Cloud Data , 2016, Remote. Sens..

[12]  Akira Kojima,et al.  Segmentation of 3D Lidar Points Using Extruded Surface of Cross Section , 2015, 2015 International Conference on 3D Vision.

[13]  Mani Golparvar-Fard,et al.  High-precision vision-based mobile augmented reality system for context-aware architectural, engineering, construction and facility management (AEC/FM) applications , 2013 .

[14]  Nassir Navab,et al.  Model globally, match locally: Efficient and robust 3D object recognition , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[15]  Juha Hyyppä,et al.  Detection of Vertical Pole-Like Objects in a Road Environment Using Vehicle-Based Laser Scanning Data , 2010, Remote. Sens..

[16]  Vladimir G. Kim,et al.  Shape-based recognition of 3D point clouds in urban environments , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[17]  Norbert Pfeifer,et al.  A Comprehensive Automated 3D Approach for Building Extraction, Reconstruction, and Regularization from Airborne Laser Scanning Point Clouds , 2008, Sensors.

[18]  Nico Blodow,et al.  Towards 3D object maps for autonomous household robots , 2007, 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[19]  Reinhard Klein,et al.  Efficient RANSAC for Point‐Cloud Shape Detection , 2007, Comput. Graph. Forum.

[20]  Robert C. Bolles,et al.  A RANSAC-Based Approach to Model Fitting and Its Application to Finding Cylinders in Range Data , 1981, IJCAI.

[21]  Qingquan Li,et al.  An Improved Method for PowerLine Reconstruction from Point Cloud Data , 2016 .