Automated Point Cloud Registration Using Visual and Planar Features for Construction Environments-Reference-Cited by-同舟云学术

Automated Point Cloud Registration Using Visual and Planar Features for Construction Environments

Published:2018-03 Issue:2 Volume:32 Page:04017076
ISSN:0887-3801
Container-title:Journal of Computing in Civil Engineering
language:en
Short-container-title:J. Comput. Civ. Eng.

Author:

Kim Pileun¹,Chen Jingdao²,Cho Yong K.³^ORCID

Affiliation:

1. Ph.D. Student, Dept. of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0355.

2. Ph.D. Student, Institute for Robotics and Intelligent Machines, Georgia Institute of Technology, 777 Atlantic Dr. N.W., Atlanta, GA 30332-0355.

3. Associate Professor, Dept. of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0355 (corresponding author). ORCID: .

Publisher

American Society of Civil Engineers (ASCE)

Subject

Computer Science Applications,Civil and Structural Engineering

Link

http://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29CP.1943-5487.0000720

Reference33 articles.

1. Bay H. Tuytelaars T. Van Gool L. Leonardis A. Bischof H. and Pinz A. (2006). “SURF: Speeded up robust features.” Computer Vision-ECCV 2006 Vol. 3951 404–417.

2. Assessment of target types and layouts in 3D laser scanning for registration accuracy

3. Besl P. and McKay N. (1992). “A method for registration of 3-D shapes.” IEEE Trans. Pattern Anal. Mach. Intell. 14(2) 239–25610.1109/34.121791.

4. Böhm J. and Becker S. (2007). “Automatic marker-free registration of terrestrial laser scans using reflectance features.” ISPRS—Proc. 8th Conf. on Optical 3-D Measurement Techniques International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences ETH Zurich Switzerland 338–344.

5. Toward automated generation of parametric BIMs based on hybrid video and laser scanning data

Cited by 38 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Automated extrinsic calibration of solid-state frame LiDAR sensors with non-overlapping field of view for monitoring indoor stockpile storage facilities;ISPRS Open Journal of Photogrammetry and Remote Sensing;2024-08

2. Multi-source point cloud registration for urban areas using a coarse-to-fine approach;GIScience & Remote Sensing;2024-04-25

3. A review of rigid point cloud registration based on deep learning;Frontiers in Neurorobotics;2024-01-04

4. Automated Point Cloud Registration Approach Optimized for a Stop-and-Go Scanning System;Sensors;2023-12-26

5. Coarse Alignment Methodology of Point Cloud Based on Camera Position/Orientation Estimation Model;Journal of Imaging;2023-12-14