Pavement 3D Data Denoising Algorithm Based on Cell Meshing Ellipsoid Detection

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As a new measuring technique, laser 3D scanning technique has advantages of rapidity, safety, and accuracy. However, the measured result of laser scanning always contains some noise points due to the measuring principle and the scanning environment. These noise points will result in the precision loss during the 3D reconstruction. The commonly used denoising algorithms ignore the strong planarity feature of the pavement, and thus might mistakenly eliminate ground points. This study proposes an ellipsoid detection algorithm to emphasize the planarity feature of the pavement during the 3D scanned data denoising process. By counting neighbors within the ellipsoid neighborhood of each point, the threshold of each point can be calculated to distinguish if it is the ground point or the noise point. Meanwhile, to narrow down the detection space and to reduce the processing time, the proposed algorithm divides the cloud point into cells. The result proves that this denoising algorithm can identify and eliminate the scattered noise points and the foreign body noise points very well, providing precise data for later 3D reconstruction of the scanned pavement.



3D reconstruction, denoising algorithm, ellipsoid neighborhood, pavement engineering, point cloud data


Yan, C.; Wei, Y.; Xiao, Y.; Wang, L. Pavement 3D Data Denoising Algorithm Based on Cell Meshing Ellipsoid Detection. Sensors 2021, 21, 2310.