Region Growing Based on 2-D–3-D Mutual Projections for Visible Point Cloud Segmentation

Zhang, Wanning; Zhou, Fuqiang; Wang, Lin; Sun, Pengfei

doi:10.1109/tim.2021.3080385

Cited by 12 publications

(3 citation statements)

References 27 publications

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“…The height difference between the road and road curbs is usually designed to be 10~25 cm, and this difference can be used for the separation of road surfaces and other points. Using this characteristic, we used the region-growing algorithm [36] based on the height difference and slope [37] limitations for roads to extract pavement points. The specific steps were as follows:…”

Section: Data Preprocessing: Registration and Pavement Point Segmenta...mentioning

confidence: 99%

Subsidence Detection for Urban Roads Using Mobile Laser Scanner Data

et al. 2022

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Pavement subsidence detection based on point cloud data acquired by mobile measurement systems is very challenging. First, the uncertainty and disorderly nature of object points data results in difficulties in point cloud comparison. Second, acquiring data with kinematic laser scanners introduces errors into systems during data acquisition, resulting in a reduction in data accuracy. Third, the high-precision measurement standard of pavement subsidence raises requirements for data processing. In this article, a data processing method is proposed to detect the subcentimeter-level subsidence of urban pavements using point cloud data comparisons in multiple time phases. The method mainly includes the following steps: First, the original data preprocessing is conducted, which includes point cloud matching and pavement point segmentation. Second, the interpolation of the pavement points into a regular grid is performed to solve the problem of point cloud comparison. Third, according to the high density of the pavement points and the performance of the pavement in the rough point cloud, using a Gaussian kernel convolution to smooth the pavement point cloud data, we aim to reduce the error in comparison. Finally, we determine the subsidence area by calculating the height difference and compare it with the threshold value. The experimental results show that the smoothing process can substantially improve the accuracy of the point cloud comparison results, effectively reducing the false detection rate and showing that subcentimeter-level pavement subsidence can be effectively detected.

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Section: Data Preprocessing: Registration and Pavement Point Segmenta...mentioning

confidence: 99%

Subsidence Detection for Urban Roads Using Mobile Laser Scanner Data

et al. 2022

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“…After obtaining the relevant data, the point cloud needs to be segmented. Common point cloud segmentation methods include clustering segmentation [6], segmentation based on region growing [7]- [9], and segmentation based on supervoxels [10]. The segmentation method based on the difference of normal vector has been applied to the extraction of urban roads and buildings [11], [12], segmentation of corn leaf points from stalk points [13] and so on.…”

Section: Introductionmentioning

confidence: 99%

A method for extracting flash point cloud feature on the surface of wind turbine blade based on difference of normal vector

Chen¹,

Huang²,

Liu³

2023

Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022)

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The surface automatic processing of large wind turbine blades has always been an industry problem. The RGBD camera can be used to scan the blade surface to obtain a 3D point cloud of complex curved surfaces. However, the point cloud contains a lot of invalid information and is unorganized data, which makes it difficult to segment the point cloud of the blade. In this paper, we propose a method based on difference of normal vector to extract the blade flash feature of wind turbine blade. Firstly, the point cloud obtained by the RGBD camera is filtered to obtain a pure wind turbine blade point cloud. Then, the difference of normal vector of the wind turbine blade point cloud is calculated to separate the flash feature from the blade surface. Finally, a pure flash point cloud is segmented by clustering, which provides guidance data for automatic grinding of flash. Through experiments, it is proved that this method can accurately segment the blade flash feature on huge wind turbine blade without a large amount of data for model training. And the separation effect is not affected by the scanning angle, sampling noise, etc. It has strong robustness.

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“…Consequently, people are no longer satisfied with using 2D images or 3D point clouds alone, and have begun to combine these two kinds of information [8]. In previous research, we proposed a visible point segmentation algorithm [9] to study the fusion of 3D point cloud data and 2D image intensity data, and built a realistic four-dimensional model (X, Y, Z, I). This algorithm combines the advantages of 2D image detection and 3D measurement, and provides a new and effective solution to the problem of object defect detection in this paper.…”

Section: Introductionmentioning

confidence: 99%

Object defect detection based on data fusion of a 3D point cloud and 2D image

Zhang¹,

Zhou²,

Liu³

et al. 2022

Meas. Sci. Technol.

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In the process of defect detection, there could be interference factors such as poor image quality and missing point cloud data as a result of the complexity of the acquisition environment. Certain limitations can be found relying solely on point cloud data processing or image feature detection. Therefore, this paper tries a more intuitive and effective exploration. Firstly, an algorithm named hole boundary points detection of point cloud based on multi-scale principal component analysis is proposed, which can achieve the preliminary detection of hole boundary points while calculating the normal vector of each point in the point cloud. Then the boundary contour of each hole is constructed by a polygon growth algorithm. Finally, we use the complementary information of three-dimensional (3D) point cloud and two-dimensional (2D) image to explore the origin of holes and realize the "true" and "false" classification of holes. The experimental results show that our algorithm can successfully detect point cloud holes and can also distinguish them from object defects, providing data support for subsequent holes filling and defect measurement.

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Region Growing Based on 2-D–3-D Mutual Projections for Visible Point Cloud Segmentation

Cited by 12 publications

References 27 publications

Subsidence Detection for Urban Roads Using Mobile Laser Scanner Data

Subsidence Detection for Urban Roads Using Mobile Laser Scanner Data

A method for extracting flash point cloud feature on the surface of wind turbine blade based on difference of normal vector

Object defect detection based on data fusion of a 3D point cloud and 2D image

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