LIDAR Point Cloud Data Extraction and Establishment of 3D Modeling of Buildings

Zhang, Yujuan; Li, Xiuhai; Wang, Qiang; Liu, Jiang; Liang, Xinlian; Li, Dan; Ni, Chundi; Liu, Yan

doi:10.1088/1757-899x/301/1/012037

Cited by 2 publications

(1 citation statement)

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“…To accurately reconstruct the step-feature lines, a seed-growth-based step-control-node tracking method [ 25 , 26 ] is introduced, as shown in Figure 8 . The method is based on the seed-growth algorithm to track out the step-control nodes in the step-feature-point set (O), and to rank the step-control nodes according to the spatial orientation.…”

Section: Methodsmentioning

confidence: 99%

Extraction of Step-Feature Lines in Open-Pit Mines Based on UAV Point-Cloud Data

Mao

Wang

et al. 2022

Sensors

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Step-feature lines are one of the important geometrical elements for drawing the status quo maps of open-pit mines, and the efficient and accurate automatic extraction and updating of step-feature lines is of great significance for open-pit-mine stripping planning and analysis. In this study, an automatic extraction method of step-feature lines in an open-pit mine based on unmanned-aerial-vehicle (UAV) point-cloud data is proposed. The method is mainly used to solve the key problems, such as low accuracy, local-feature-line loss, and the discontinuity of the step-feature-line extraction method. The method first performs the regular raster resampling of the open-pit-mine cloud based on the MLS algorithm, then extracts the step-feature point set by detecting the elevation-gradient change in the resampled point cloud, further traces the step-feature control nodes by the seed-growth tracking algorithm, and finally generates smooth step-feature lines by fitting the space curve to the step-feature control nodes. The results show that the method effectively improves the accuracy of step-feature-line extraction and solves the problems of local-feature-line loss and discontinuity.

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Section: Methodsmentioning

confidence: 99%