4-Plane congruent sets for automatic registration of as-is 3D point clouds with 3D BIM models

2018 ◽  
Vol 89 ◽  
pp. 120-134 ◽  
Author(s):  
Martín Bueno ◽  
Frédéric Bosché ◽  
Higinio González-Jorge ◽  
Joaquín Martínez-Sánchez ◽  
Pedro Arias
Keyword(s):  
Point Clouds ◽  
Automatic Registration ◽  
3D Point Clouds

Registration of 3D Point Clouds for Ship Block Measurement

2015 ◽  
Author(s):  
Sunghan Kim ◽  
Mingyu Kim ◽  
Jeongtae Lee ◽  
Jinhwi Pyo ◽  
Heeyoung Heo ◽  
...  
Keyword(s):  
Point Cloud ◽  
Measurement Data ◽  
User Interaction ◽  
Point Clouds ◽  
Processing Unit ◽  
Feature Points ◽  
Automatic Registration ◽  
Actual Measurement ◽  
Large Size ◽  
3D Point Clouds

In this paper, a software system for registration of point clouds is developed. The system consists of two modules for registration and user interaction. The registration module contains functions for manual and automatic registration. The manual method allows a user to select feature points or planes from the point clouds manually. The selected planes or features are then processed to establish the correspondence between the point clouds, and registration is performed to obtain one large point cloud. The automatic registration uses sphere targets. Sphere targets are attached to an object of interest. A scanner measures the object as well as the targets to produce point clouds, from which the targets are extracted using shape intrinsic properties. Then correspondence between the point clouds is obtained using the targets, and the registration is performed. The user interaction module provides a GUI environment which allows a user to navigate point clouds, to compute various features, to visualize point clouds and to select/unselect points interactively and the point-processing unit containing functions for filtering, estimation of geometric features, and various data structures for managing point clouds of large size. The developed system is tested with actual measurement data of various blocks in a shipyard.

scholarly journals AN IMPROVED COHERENT POINT DRIFT METHOD FOR TLS POINT CLOUD REGISTRATION OF COMPLEX SCENES

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 1169-1175
Author(s):  
Y. Zang ◽  
R. C. Lindenbergh
Keyword(s):  
Likelihood Function ◽  
Point Clouds ◽  
Automatic Registration ◽  
Covariance Descriptor ◽  
Uniform Sampling ◽  
3D Point Clouds ◽  
Complex Scenes ◽  
Subsequent Step ◽  
Coherent Point Drift ◽  
Normal Vectors

<p><strong>Abstract.</strong> Processing unorganized 3D point clouds is highly desirable, especially for the applications in complex scenes (such as: mountainous or vegetation areas). Registration is the precondition to obtain complete surface information of complex scenes. However, for complex environment, the automatic registration of TLS point clouds is still a challenging problem. In this research, we propose an automatic registration for TLS point clouds of complex scenes based on coherent point drift (CPD) algorithm combined with a robust covariance descriptor. Out method consists of three steps: the construction of the covariance descriptor, uniform sampling of point clouds, and CPD optimization procedures based on Expectation-Maximization (EM algorithm). In the first step, we calculate a feature vector to construct a covariance matrix for each point based on the estimated normal vectors. In the subsequent step, to ensure efficiency, we use uniform sampling to obtain a small point set from the original TLS data. Finally, we form an objective function combining the geometric information described by the proposed descriptor, and optimize the transformation iteratively by maximizing the likelihood function. The experimental results on the TLS datasets of various scenes demonstrate the reliability and efficiency of the proposed method. Especially for complex environments with disordered vegetation or point density variations, this method can be much more efficient than original CPD algorithm.</p>

scholarly journals Symmetry Analysis of Oriental Polygonal Pagodas Using 3D Point Clouds for Cultural Heritage

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1228
Author(s):  
Ting On Chan ◽  
Linyuan Xia ◽  
Yimin Chen ◽  
Wei Lang ◽  
Tingting Chen ◽  
...  
Keyword(s):  
Cultural Heritage ◽  
Unmanned Aerial Vehicle ◽  
Point Cloud ◽  
Geometric Model ◽  
Digital Camera ◽  
Point Clouds ◽  
Symmetry Analysis ◽  
3D Point Clouds ◽  
Transmission Towers ◽  
Aerial Vehicle

Ancient pagodas are usually parts of hot tourist spots in many oriental countries due to their unique historical backgrounds. They are usually polygonal structures comprised by multiple floors, which are separated by eaves. In this paper, we propose a new method to investigate both the rotational and reflectional symmetry of such polygonal pagodas through developing novel geometric models to fit to the 3D point clouds obtained from photogrammetric reconstruction. The geometric model consists of multiple polygonal pyramid/prism models but has a common central axis. The method was verified by four datasets collected by an unmanned aerial vehicle (UAV) and a hand-held digital camera. The results indicate that the models fit accurately to the pagodas’ point clouds. The symmetry was realized by rotating and reflecting the pagodas’ point clouds after a complete leveling of the point cloud was achieved using the estimated central axes. The results show that there are RMSEs of 5.04 cm and 5.20 cm deviated from the perfect (theoretical) rotational and reflectional symmetries, respectively. This concludes that the examined pagodas are highly symmetric, both rotationally and reflectionally. The concept presented in the paper not only work for polygonal pagodas, but it can also be readily transformed and implemented for other applications for other pagoda-like objects such as transmission towers.

scholarly journals Using Least-Squares Residuals to Assess the Stochasticity of Measurements—Example: Terrestrial Laser Scanner and Surface Modeling

2021 ◽  
Vol 5 (1) ◽  
pp. 59
Author(s):  
Gaël Kermarrec ◽  
Niklas Schild ◽  
Jan Hartmann
Keyword(s):  
Least Squares ◽  
Laser Scanner ◽  
Real Data ◽  
Point Clouds ◽  
Statistical Testing ◽  
Normal Vector ◽  
Engineering Structures ◽  
3D Point Clouds ◽  
Least Squares Adjustment ◽  
Correlation Parameters

Terrestrial laser scanners (TLS) capture a large number of 3D points rapidly, with high precision and spatial resolution. These scanners are used for applications as diverse as modeling architectural or engineering structures, but also high-resolution mapping of terrain. The noise of the observations cannot be assumed to be strictly corresponding to white noise: besides being heteroscedastic, correlations between observations are likely to appear due to the high scanning rate. Unfortunately, if the variance can sometimes be modeled based on physical or empirical considerations, the latter are more often neglected. Trustworthy knowledge is, however, mandatory to avoid the overestimation of the precision of the point cloud and, potentially, the non-detection of deformation between scans recorded at different epochs using statistical testing strategies. The TLS point clouds can be approximated with parametric surfaces, such as planes, using the Gauss–Helmert model, or the newly introduced T-splines surfaces. In both cases, the goal is to minimize the squared distance between the observations and the approximated surfaces in order to estimate parameters, such as normal vector or control points. In this contribution, we will show how the residuals of the surface approximation can be used to derive the correlation structure of the noise of the observations. We will estimate the correlation parameters using the Whittle maximum likelihood and use comparable simulations and real data to validate our methodology. Using the least-squares adjustment as a “filter of the geometry” paves the way for the determination of a correlation model for many sensors recording 3D point clouds.

A method of calculating the leafstalk angle of the soybean canopy based on 3D point clouds

2021 ◽  
Vol 42 (7) ◽  
pp. 2463-2484
Author(s):  
Kexin Zhu ◽  
Xiaodan Ma ◽  
Haiou Guan ◽  
Jiarui Feng ◽  
Zhichao Zhang ◽  
...  
Keyword(s):  
Point Clouds ◽  
3D Point Clouds ◽  
Soybean Canopy
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