A Simplification Method for Point Cloud of T-Profile Steel Plate for Shipbuilding

According to the requirements of point cloud simplification for T-profile steel plate welding in shipbuilding, the disadvantages of the existing simplification algorithms are analyzed. In this paper, a point cloud simplification method is proposed based on octree coding and the threshold of the surface curvature feature. In this method, the original point cloud data are divided into multiple sub-cubes with specified side lengths by octree coding, and the points that are closest to the gravity center of the sub-cube are kept. The k-neighborhood method and the curvature calculation are performed in order to obtain the curvature features of the point cloud. Additionally, the point cloud data are divided into several regions based on the given adjustable curvature threshold. Finally, combining the random sampling method with the simplification method based on the regional gravity center, the T-profile point cloud data can be simplified. In this study, after obtaining the point cloud data of a T-profile plate, the proposed simplification method is compared with some other simplification methods. It is found that the proposed simplification method for the point cloud of the T-profile steel plate for shipbuilding is faster than the three existing simplification methods, while retaining more feature points and having approximately the same reduction rates.

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3D Remodeling of Molds Broken Area Based on Reverse Engineering

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.860-863.2640 ◽

2013 ◽

Vol 860-863 ◽

pp. 2640-2643

Author(s):

Jin Hua Li ◽

De Qiang Zhang ◽

Jian Li Zhang ◽

Jie Cheng ◽

Fang Ping Yao

Keyword(s):

Reverse Engineering ◽

Traditional Method ◽

Point Cloud ◽

Sampling Method ◽

Surface Model ◽

Point Cloud Data ◽

Important Data ◽

Engineering Technology ◽

Cloud Data ◽

Contact Measurement

The mold’s broken area is irregular, so it is difficult to model by traditional method. It puts forwards to remodel the mold’s broken area by Reverse Engineering technology. Non-contact measurement and contact measurement are both used to measure the broken mold. The pretreatment tasks are done including the point cloud data reduction Chord where the deviation sampling method is used. The surface model of the mold is remodeled and solidified. The broken area is extracted and solidified, and it provides the important data for the following repairing work.

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Estimation of shoulder width and neck girth based on 3D point cloud data

Thermal Science ◽

10.2298/tsci200311047l ◽

2021 ◽

pp. 47-47

Author(s):

Xin Lu ◽

Panpan Guo ◽

Guolian Liu

Keyword(s):

Point Cloud ◽

Three Dimensional ◽

Young Female ◽

3D Measurement ◽

3D Point Cloud ◽

Point Cloud Data ◽

Cloud Data ◽

Point Cloud Simplification

Three dimensional point cloud map in the anthropometry has attracted intensive attention due to the availability of fast and accurate laser scan devices. Inevitably, there is a data deviation between 3D measurement and manual tests. To address this problem, shoulder width and neck girth are accurately determined from 3D point cloud, the two-scale fractal is used for 3D point cloud simplification, and young female samples are used in our experiment to show the accuracy.

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An Iterative Coarse-to-Fine Sub-Sampling Method for Density Reduction of Terrain Point Clouds

Remote Sensing ◽

10.3390/rs11080947 ◽

2019 ◽

Vol 11 (8) ◽

pp. 947 ◽

Cited By ~ 2

Author(s):

Lei Fan ◽

Peter M. Atkinson

Keyword(s):

Point Cloud ◽

Laser Scanning ◽

Sampling Method ◽

Computational Cost ◽

Point Clouds ◽

Free Access ◽

Point Cloud Data ◽

Cloud Data ◽

Data Density ◽

Coarse To Fine

Point clouds obtained from laser scanning techniques are now a standard type of spatial data for characterising terrain surfaces. Some have been shared as open data for free access. A problem with the use of these free point cloud data is that the data density may be more than necessary for a given application, leading to higher computational cost in subsequent data processing and visualisation. In such cases, to make the dense point clouds more manageable, their data density can be reduced. This research proposes a new coarse-to-fine sub-sampling method for reducing point cloud data density, which honours the local surface complexity of a terrain surface. The method proposed is tested using four point clouds representing terrain surfaces with distinct spatial characteristics. The effectiveness of the iterative coarse-to-fine method is evaluated and compared against several benchmarks in the form of typical sub-sampling methods available in open source software for point cloud processing.

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Analysis of Efficient Visualization and Interactive Editing of 3D Point Cloud Data

TECHART Journal of Arts and Imaging Science ◽

10.15323/techart.2016.05.3.2.32 ◽

2016 ◽

Vol 3 (2) ◽

pp. 32

Author(s):

Rekha Patil ◽

Ashok Kumar Patil ◽

Youngho Chai

Keyword(s):

Point Cloud ◽

3D Point Cloud ◽

Point Cloud Data ◽

Cloud Data ◽

Interactive Editing

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Registration and Fusion of UAV LiDAR System Sequence Images and Laser Point Clouds

Journal of Imaging Science and Technology ◽

10.2352/j.imagingsci.technol.2021.65.1.01050110.2352/j.imagingsci.technol.2021.65.1.010501 ◽

2020 ◽

Author(s):

Jiayong Yu ◽

Longchen Ma ◽

Maoyi Tian, ◽

Xiushan Lu

Keyword(s):

Point Cloud ◽

Image Data ◽

Point Clouds ◽

Optical Image ◽

Point Cloud Data ◽

Feature Points ◽

Lidar System ◽

Registration Accuracy ◽

Cloud Data ◽

Intensity Image

The unmanned aerial vehicle (UAV)-mounted mobile LiDAR system (ULS) is widely used for geomatics owing to its efficient data acquisition and convenient operation. However, due to limited carrying capacity of a UAV, sensors integrated in the ULS should be small and lightweight, which results in decrease in the density of the collected scanning points. This affects registration between image data and point cloud data. To address this issue, the authors propose a method for registering and fusing ULS sequence images and laser point clouds, wherein they convert the problem of registering point cloud data and image data into a problem of matching feature points between the two images. First, a point cloud is selected to produce an intensity image. Subsequently, the corresponding feature points of the intensity image and the optical image are matched, and exterior orientation parameters are solved using a collinear equation based on image position and orientation. Finally, the sequence images are fused with the laser point cloud, based on the Global Navigation Satellite System (GNSS) time index of the optical image, to generate a true color point cloud. The experimental results show the higher registration accuracy and fusion speed of the proposed method, thereby demonstrating its accuracy and effectiveness.

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