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

Abstract

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.

Improved 3D Pavement Texture Reconstruction Method Based on Interference Fringe via Optimizing the Post-Processing Method

“…If measuring engineering products with larger sizes, such as pavement surfaces, however, the traditional post-processing method will cause the reconstructed surface to slant due to the different laser incident angle. In order to make the 3D reconstruction more accurate, optimization for the post-processing method is indispensable [ 21 ]. The innovation of this research is to solve this problem of slant-reconstructed surface by optimizing the post-processing method of 3D-PTRIF to make it possible to measure a pavement surface with larger area more accurately.…”