An Accurate and Robust Region-Growing Algorithm for Plane Segmentation of TLS Point Clouds Using a Multiscale Tensor Voting Method

Wu, Hao; Zhang, Xiang; Shi, Wenzhong; Song, Shaoxian; Tristán, Abraham Cárdenas; Li, Kui

doi:10.1109/jstars.2019.2936662

Cited by 24 publications

(14 citation statements)

References 24 publications

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“…The convergence of the algorithm or energy function was ensured by the simulated annealing approach. Wu et al [ 47 ] presented the planar segmentation of Laser Range Finder point clouds using the MSTVM (Multiscale Tensor Voting Method) to better determine the point that represents the seed of the algorithm. They introduced a new property (the so-called plane strength indicator) to determine the seed point more intuitively.…”

Section: Related Workmentioning

confidence: 99%

Depth-Image Segmentation Based on Evolving Principles for 3D Sensing of Structured Indoor Environments

Antić

Zdešar

Škrjanc

2021

Sensors

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This paper presents an approach of depth image segmentation based on the Evolving Principal Component Clustering (EPCC) method, which exploits data locality in an ordered data stream. The parameters of linear prototypes, which are used to describe different clusters, are estimated in a recursive manner. The main contribution of this work is the extension and application of the EPCC to 3D space for recursive and real-time detection of flat connected surfaces based on linear segments, which are all detected in an evolving way. To obtain optimal results when processing homogeneous surfaces, we introduced two-step filtering for outlier detection within a clustering framework and considered the noise model, which allowed for the compensation of characteristic uncertainties that are introduced into the measurements of depth sensors. The developed algorithm was compared with well-known methods for point cloud segmentation. The proposed approach achieves better segmentation results over longer distances for which the signal-to-noise ratio is low, without prior filtering of the data. On the given database, an average rate higher than 90% was obtained for successfully detected flat surfaces, which indicates high performance when processing huge point clouds in a non-iterative manner.

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Section: Related Workmentioning

confidence: 99%

Depth-Image Segmentation Based on Evolving Principles for 3D Sensing of Structured Indoor Environments

Antić

Zdešar

Škrjanc

2021

Sensors

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“…However, there are few obvious geographic or architectural features in highway and any two site clouds are very similar, which make the ICP registration method unsuitable for highway point cloud registration. In addition, some novel and effective algorithms for planar registration and segmentation have been proposed recently [16]- [18], such as unsupervised robust planar segmentation, procedural shape priors-based building facades segmentation and a multiscale tensor voting method. Especially, Wu et al [18] improve the selection of seed points in region-growing algorithm and obtain comprehensive planar segmentation results, which are more robust than clustering-based methods.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, some novel and effective algorithms for planar registration and segmentation have been proposed recently [16]- [18], such as unsupervised robust planar segmentation, procedural shape priors-based building facades segmentation and a multiscale tensor voting method. Especially, Wu et al [18] improve the selection of seed points in region-growing algorithm and obtain comprehensive planar segmentation results, which are more robust than clustering-based methods. Yet, the uneven deformation monitoring of the highway bridge head requires lower plane accuracy, which is generally about ±2cm, and higher elevation accuracy, which is generally better than ±5mm [19].…”

Section: Introductionmentioning

confidence: 99%

Deformation Measurement of Highway Bridge Head Based on Mobile TLS Data

et al. 2020

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The precise leveling method is often used to monitor the uneven deformation of the highway bridge head, which requires manual contact, dangerous and heavy workload. In order to solve these issues, this paper proposes a method to extract deformation features of the highway bridge head based on mobile terrestrial laser scanning (TLS) point clouds. Firstly, an automatic data acquisition system is designed to analyze and determinate the scanning station spacing and scanning resolution. Then acquired road point clouds are denoised based on the plane fitting method, which usually sets the experimental threshold. The road dividing line information is used for point clouds coarse registration, and the weighted average elevation method is used for refined registration. Lastly, the elevation of the deformation monitoring point is the average elevation of all points in the selected grid surface, which will mitigate random errors in the elevation of a single point. Point clouds of three different roads were collected to verify the proposed method. The results show that the accuracy of the elevation repeatability is better than ± 1mm, and the accuracy of the elevation check with the TM50 total station is better than ± 2mm, which meets the requirements of deformation monitoring. In addition, it takes about 3-4 minutes to complete the data collection of a station on the highway bridge head. Therefore, the proposed method based on mobile TLS data can be suitable for highway bridge head deformation measurement.INDEX TERMS Terrestrial laser scanning (TLS), highway bridge head, deformation monitoring, automatic data acquiring system, point cloud processing.

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“…At present, the methods for obtaining the characteristics of urban PM2.5 concentration mainly include airborne remote sensing and ground sensor monitoring [6][7][8][9][10]. Airborne platforms have the advantage of a wide monitoring range, fast information acquisition, and low cost, which have been widely used in many countries and regions around the world.…”

Section: Introductionmentioning

confidence: 99%

Assessing Spatiotemporal Characteristics of Urban PM2.5 Using Fractal Dimensions and Wavelet Analysis

Wang

Zhang

Liang

et al. 2020

Mathematical Problems in Engineering

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Due to rapid urbanization and industrialization, atmospheric fine particulate matter (PM2.5) has become a primary urban pollutant, seriously affecting air quality and resident health. Existing airborne remote sensing and ground sensor monitoring can efficiently collect PM2.5 data. It is urgent yet challenging to fully use these two monitoring modes to analyze the spatial distribution and dynamic changes of PM2.5. This paper proposes a method to analyze the spatiotemporal characteristics of urban PM2.5 concentration using airborne and ground monitoring data. The method utilizes the boundary dimension and the radius dimension to describe the spatial distribution characteristics of PM2.5 and then adopts wavelet analysis to detect the fluctuation periods of PM2.5 concentration. Case study was performed based on the moderate-resolution imaging spectroradiometer (MODIS) data and the daily ground monitoring concentration of PM2.5 from 2014 to 2017 in Beijing. The decrease results of boundary dimension indicate that the changes in PM2.5 concentration tend to be slower over time, and the fluctuation amplitude gradually becomes smaller. The increase results closer to 2.0 in the radius dimensions suggest that the PM2.5 distribution becomes more uniform, indicating that the pollution control in Beijing had achieved initial success. The frequency results of wavelet analysis reveal that PM2.5 concentration in Beijing is mainly subject to periodic variation of 190 days. These findings can help us to gain deeper insight into the complex spatiotemporal characteristics of urban PM2.5.

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An Accurate and Robust Region-Growing Algorithm for Plane Segmentation of TLS Point Clouds Using a Multiscale Tensor Voting Method

Cited by 24 publications

References 24 publications

Depth-Image Segmentation Based on Evolving Principles for 3D Sensing of Structured Indoor Environments

Depth-Image Segmentation Based on Evolving Principles for 3D Sensing of Structured Indoor Environments

Deformation Measurement of Highway Bridge Head Based on Mobile TLS Data

Assessing Spatiotemporal Characteristics of Urban PM2.5 Using Fractal Dimensions and Wavelet Analysis

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