scholarly journals A Modified Iterative Closest Point Algorithm for 3D Point Cloud Registration

2016 ◽  
Vol 31 (7) ◽  
pp. 515-534 ◽  
Author(s):  
Roberto Marani ◽  
Vito Renò ◽  
Massimiliano Nitti ◽  
Tiziana D'Orazio ◽  
Ettore Stella
Keyword(s):  
Point Cloud ◽  
Iterative Closest Point ◽  
3D Point Cloud ◽  
Point Cloud Registration ◽  
Iterative Closest Point Algorithm

scholarly journals A Maximum Feasible Subsystem for Globally Optimal 3D Point Cloud Registration

Sensors ◽  
2018 ◽  
Vol 18 (2) ◽  
pp. 544 ◽  
Author(s):  
Chanki Yu ◽  
Da Ju
Keyword(s):  
Point Cloud ◽  
3D Point Cloud ◽  
Point Cloud Registration

scholarly journals ITERATIVE CLOSEST POINT ALGORITHM FOR ACCURATE REGISTRATION OF COARSELY REGISTERED POINT CLOUDS WITH CITYGML MODELS

2019 ◽  
Vol IV-2/W5 ◽  
pp. 201-208 ◽  
Author(s):  
S. Goebbels ◽  
R. Pohle-Fröhlich ◽  
P. Pricken
Keyword(s):  
Point Cloud ◽  
Point Clouds ◽  
Iteration Step ◽  
Iterative Closest Point ◽  
Target Point ◽  
Standard Tool ◽  
Iterative Closest Point Algorithm ◽  
Test Scenarios ◽  
Plane Mode ◽  
City Models

<p><strong>Abstract.</strong> The Iterative Closest Point algorithm (ICP) is a standard tool for registration of a source to a target point cloud. In this paper, ICP in point-to-plane mode is adopted to city models that are defined in CityGML. With this new point-to-model version of the algorithm, a coarsely registered photogrammetric point cloud can be matched with buildings’ polygons to provide, e.g., a basis for automated 3D facade modeling. In each iteration step, source points are projected to these polygons to find correspondences. Then an optimization problem is solved to find an affine transformation that maps source points to their correspondences as close as possible. Whereas standard ICP variants do not perform scaling, our algorithm is capable of isotropic scaling. This is necessary because photogrammetric point clouds obtained by the structure from motion algorithm typically are scaled randomly. Two test scenarios indicate that the presented algorithm is faster than ICP in point-to-plane mode on sampled city models.</p>

Local feature extraction network with high correspondences for 3d point cloud registration

2022 ◽  
Author(s):  
Dashuang Li ◽  
Kai He ◽  
Lei Wang ◽  
Dazhuang Zhang
Keyword(s):  
Feature Extraction ◽  
Point Cloud ◽  
Local Feature ◽  
3D Point Cloud ◽  
Point Cloud Registration ◽  
Local Feature Extraction

FPFH-based graph matching for 3D point cloud registration

2018 ◽  
Author(s):  
Jiapeng Zhao ◽  
Chen Li ◽  
Lihua Tian ◽  
Jihua Zhu
Keyword(s):  
Point Cloud ◽  
Graph Matching ◽  
3D Point Cloud ◽  
Point Cloud Registration
Sign in / Sign up

Export Citation Format

Share Document