scholarly journals Scan registration using planar features

2014 ◽  
Vol XL-5 ◽  
pp. 501-508 ◽  
Author(s):  
M. Previtali ◽  
L. Barazzetti ◽  
R. Brumana ◽  
M. Scaioni
Keyword(s):  
Point Cloud ◽  
Urban Environments ◽  
Registration Method ◽  
Large Structures ◽  
Laser Scanners ◽  
Scan Registration ◽  
Feature Based ◽  
Automatic Methods ◽  
Rigid Body Transformation ◽  
Planar Feature

Point cloud acquisition by using laser scanners provides an efficient way for 3D as-built modelling of indoor/outdoor urban environments. In the case of large structures, multiple scans may be required to cover the entire scene and registration is needed to merge them together. In general, the identification of corresponding geometric features among a series of scans can be used to compute the 3D rigid-body transformation useful for the registration of each scan into the reference system of the final point cloud. Different automatic or semi-automatic methods have been developed to this purpose. Several solutions based on artificial targets are available, which however may not be suitable in any situations. Methods based on surface matching (like ICP and LS3D) can be applied if the scans to align have a proper geometry and surface texture. In the case of urban and architectural scenes that present the prevalence of a few basic geometric shapes ("Legoland" scenes) the availability of many planar features is exploited here for registration. The presented technique does not require artificial targets to be added to the scanned scene. In addition, unlike other surface-based techniques (like ICP) the planar feature-based registration technique is not limited to work in a pairwise manner but it can handle the simultaneous alignment of multiple scans. Finally, some applications are presented and discussed to show how this technique can achieve accuracy comparable to a consolidated registration method.

A Fast and Accurate Planar-Feature-Based Global Scan Registration Method

2019 ◽  
Vol 19 (24) ◽  
pp. 12333-12345
Author(s):  
Wenpeng Zong ◽  
Minglei Li ◽  
Yanglin Zhou ◽  
Li Wang ◽  
Fengzhuo Xiang ◽  
...  
Keyword(s):  
Registration Method ◽  
Scan Registration ◽  
Feature Based ◽  
Planar Feature

scholarly journals TRAJECTORY-BASED VISUALIZATION OF MMS POINT CLOUDS

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 1127-1133
Author(s):  
G. Takahashi ◽  
H. Masuda
Keyword(s):  
Point Cloud ◽  
Point Clouds ◽  
Automatic Classification ◽  
Infrastructure Management ◽  
Classification Methods ◽  
Main Road ◽  
Laser Scanners ◽  
Map Generation ◽  
Automatic Methods ◽  
3D Information

<p><strong>Abstract.</strong> MMSs allow us to obtain detailed 3D information around roads. Especially, LiDAR point clouds can be used for map generation and infrastructure management. For practical uses, however, it is necessary to add labels to a part of the points since various objects can be included in the point clouds. Existing automatic classification methods are not completely error-free, and may incorrectly classify objects. Therefore, even though automatic methods are applied to the point clouds, operators have to verify the labels. While operators classify the point clouds manually, selecting 3D points tasks in 3D views are difficult. In this paper, we propose a new point-cloud image based on the trajectories of MMSs. We call our point-cloud image <i>trajectory-based point-cloud image</i>. Although the image is distorted because it is generated based on rotation angles of laser scanners, we confirmed that most objects can be recognized from point-cloud images by checking main road facilities. We evaluated how efficient the annotation can be done using our method, and the results show that operators could add annotations to point-cloud images more efficiently.</p>

scholarly journals A Registration Method Based on Contour Point Cloud for 3D Whole-Body PET and CT Images

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Zhiying Song ◽  
Huiyan Jiang ◽  
Qiyao Yang ◽  
Zhiguo Wang ◽  
Guoxu Zhang
Keyword(s):  
Point Cloud ◽  
Point Clouds ◽  
Ct Images ◽  
Whole Body ◽  
Geometric Feature ◽  
Registration Method ◽  
Feature Based ◽  
Contour Point ◽  
Clinical Meaning ◽  
Ct Data

The PET and CT fusion image, combining the anatomical and functional information, has important clinical meaning. An effective registration of PET and CT images is the basis of image fusion. This paper presents a multithread registration method based on contour point cloud for 3D whole-body PET and CT images. Firstly, a geometric feature-based segmentation (GFS) method and a dynamic threshold denoising (DTD) method are creatively proposed to preprocess CT and PET images, respectively. Next, a new automated trunk slices extraction method is presented for extracting feature point clouds. Finally, the multithread Iterative Closet Point is adopted to drive an affine transform. We compare our method with a multiresolution registration method based on Mattes Mutual Information on 13 pairs (246~286 slices per pair) of 3D whole-body PET and CT data. Experimental results demonstrate the registration effectiveness of our method with lower negative normalization correlation (NC = −0.933) on feature images and less Euclidean distance error (ED = 2.826) on landmark points, outperforming the source data (NC = −0.496, ED = 25.847) and the compared method (NC = −0.614, ED = 16.085). Moreover, our method is about ten times faster than the compared one.

Research on Three-Dimensional Reconstruction Technology Based on the Data of Hybrid Measurement

2014 ◽  
Vol 1039 ◽  
pp. 30-35
Author(s):  
Wei Liu ◽  
Lu Yue Ju ◽  
Cheng Hui Lin
Keyword(s):  
Point Cloud ◽  
Three Dimensional ◽  
Point Clouds ◽  
Parametric Modeling ◽  
Three Dimensional Reconstruction ◽  
Three Dimensional Model ◽  
Registration Method ◽  
Engine Part ◽  
Dimensional Reconstruction ◽  
Feature Based

Hybrid measurement method is proposed to solve the problem that the partial or whole three-dimensional reconstruction accuracy of aviation engine parts is high. The point clouds of the aviation engine part are captured first using contact and non-contact measuring method. Feature-based parametric modeling strategy is adopted to reconstruct the aviation engine part so that it is easy to be modified in the future. Then, the point cloud data obtained by contact measurement and the reconstructed model are registrated to the same coordinate system to detect the deviation. The point cloud registration method is based upon the feature-based registration method and standard Iterative Closest Point (ICP) algorithm, which help to improve the accuracy of registration. According to the result of deviation, the three-dimensional model can be modified. The accuracy of the modified model is controlled within 0.02mm, satisfying the requirement of aviation engine parts. Three-dimensional reconstruction results have verified the feasibility of the method.

Registration of Point-Clouds from Terrestrial and Portable Laser Scanners

2016 ◽  
Vol 10 (2) ◽  
pp. 163-171 ◽  
Author(s):  
Takuma Watanabe ◽  
◽  
Takeru Niwa ◽  
Hiroshi Masuda ◽  
Keyword(s):  
Point Cloud ◽  
Short Range ◽  
Large Scale ◽  
Laser Scanner ◽  
Point Clouds ◽  
Limited Region ◽  
Registration Method ◽  
Laser Scanners ◽  
Scale Point ◽  
Candidate Regions

We proposed a registration method for aligning short-range point-clouds captured using a portable laser scanner (PLS) to a large-scale point-cloud captured using a terrestrial laser scanner (TLS). As a PLS covers a very limited region, it often fails to provide sufficient features for registration. In our method, the system analyzes large-scale point-clouds captured using a TLS and indicates candidate regions to be measured using a PLS. When the user measures a suggested region, the system aligns the captured short-range point-cloud to the large-scale point-cloud. Our experiments show that the registration method can adequately align point-clouds captured using a TLS and a PLS.

3D Registration Based on Planar Feature Segmentation and Plane Fit

2012 ◽  
Vol 233 ◽  
pp. 274-277
Author(s):  
Hong Ke Wang ◽  
Xiao Feng Wang
Keyword(s):  
Computer Vision ◽  
Virtual Reality ◽  
3D Reconstruction ◽  
Range Image ◽  
Coordinate Systems ◽  
3D Registration ◽  
Registration Method ◽  
Feature Segmentation ◽  
Feature Based ◽  
Planar Feature

3D reconstruction is used in applications such as virtual reality, digital cinematography and urban planning .The 3D registration is the important part of 3D reconstruction, which is one of outstanding and very basic problems in computer vision. In the paper, considering that there often exist a great number of planes in scenes, we show a planar-feature-based registration method. The planar features from the range image are extracted. Then, we can compute the transformation by SVD between the two coordinate systems and achieve the registration of these two range images.

A Proposal of 3D Feature based on Occupancy of Point Cloud in Multi-Scale Shell Region

2016 ◽  
Vol 136 (8) ◽  
pp. 1078-1084
Author(s):  
Shoichi Takei ◽  
Shuichi Akizuki ◽  
Manabu Hashimoto
Keyword(s):  
Point Cloud ◽  
Multi Scale ◽  
Feature Based ◽  
Shell Region

Handheld Laser Scanner Research

2019 ◽  
Vol 952 (10) ◽  
pp. 47-54
Author(s):  
A.V. Komissarov ◽  
A.V. Remizov ◽  
M.M. Shlyakhova ◽  
K.K. Yambaev
Keyword(s):  
Point Cloud ◽  
Three Dimensional ◽  
Laser Scanner ◽  
Test Site ◽  
Optical Systems ◽  
Advantages And Disadvantages ◽  
Site Survey ◽  
Laser Scanners ◽  
Three Dimensional Models ◽  
The Stability

The authors consider hand-held laser scanners, as a new photogrammetric tool for obtaining three-dimensional models of objects. The principle of their work and the newest optical systems based on various sensors measuring the depth of space are described in detail. The method of simultaneous navigation and mapping (SLAM) used for combining single scans into point cloud is outlined. The formulated tasks and methods for performing studies of the DotProduct (USA) hand-held laser scanner DPI?8X based on a test site survey are presented. The accuracy requirements for determining the coordinates of polygon points are given. The essence of the performed experimental research of the DPI?8X scanner is described, including scanning of a test object at various scanner distances, shooting a test polygon from various scanner positions and building point cloud, repeatedly shooting the same area of the polygon to check the stability of the scanner. The data on the assessment of accuracy and analysis of research results are given. Fields of applying hand-held laser scanners, their advantages and disadvantages are identified.

scholarly journals Linear-Based Incremental Co-Registration of MLS and Photogrammetric Point Clouds

2021 ◽  
Vol 13 (11) ◽  
pp. 2195
Author(s):  
Shiming Li ◽  
Xuming Ge ◽  
Shengfu Li ◽  
Bo Xu ◽  
Zhendong Wang
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Point Clouds ◽  
Registration Method ◽  
Cloud Data ◽  
Linear Feature ◽  
Data Registration ◽  
Source Point ◽  
Urban Remote Sensing ◽  
Single Data

Today, mobile laser scanning and oblique photogrammetry are two standard urban remote sensing acquisition methods, and the cross-source point-cloud data obtained using these methods have significant differences and complementarity. Accurate co-registration can make up for the limitations of a single data source, but many existing registration methods face critical challenges. Therefore, in this paper, we propose a systematic incremental registration method that can successfully register MLS and photogrammetric point clouds in the presence of a large number of missing data, large variations in point density, and scale differences. The robustness of this method is due to its elimination of noise in the extracted linear features and its 2D incremental registration strategy. There are three main contributions of our work: (1) the development of an end-to-end automatic cross-source point-cloud registration method; (2) a way to effectively extract the linear feature and restore the scale; and (3) an incremental registration strategy that simplifies the complex registration process. The experimental results show that this method can successfully achieve cross-source data registration, while other methods have difficulty obtaining satisfactory registration results efficiently. Moreover, this method can be extended to more point-cloud sources.

scholarly journals A Closed-Form Solution to Planar Feature-Based Registration of LiDAR Point Clouds

2021 ◽  
Vol 10 (7) ◽  
pp. 435
Author(s):  
Yongbo Wang ◽  
Nanshan Zheng ◽  
Zhengfu Bian
Keyword(s):  
Closed Form ◽  
Closed Form Solution ◽  
Simulated Data ◽  
Real Data ◽  
Point Clouds ◽  
Form Solution ◽  
Spatial Transformation ◽  
Dual Quaternions ◽  
Feature Based ◽  
Planar Feature

Since pairwise registration is a necessary step for the seamless fusion of point clouds from neighboring stations, a closed-form solution to planar feature-based registration of LiDAR (Light Detection and Ranging) point clouds is proposed in this paper. Based on the Plücker coordinate-based representation of linear features in three-dimensional space, a quad tuple-based representation of planar features is introduced, which makes it possible to directly determine the difference between any two planar features. Dual quaternions are employed to represent spatial transformation and operations between dual quaternions and the quad tuple-based representation of planar features are given, with which an error norm is constructed. Based on L2-norm-minimization, detailed derivations of the proposed solution are explained step by step. Two experiments were designed in which simulated data and real data were both used to verify the correctness and the feasibility of the proposed solution. With the simulated data, the calculated registration results were consistent with the pre-established parameters, which verifies the correctness of the presented solution. With the real data, the calculated registration results were consistent with the results calculated by iterative methods. Conclusions can be drawn from the two experiments: (1) The proposed solution does not require any initial estimates of the unknown parameters in advance, which assures the stability and robustness of the solution; (2) Using dual quaternions to represent spatial transformation greatly reduces the additional constraints in the estimation process.

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