Due to the external environment, the discreteness of the laser beam, occlusion, there may be noisy points and outliers in the point cloud data that collected by the 3D laser scanner. These defects can degrade the quality of the point cloud and affect the accuracy of the subsequent three-dimensional point cloud of surface reconstruction. The 5%, 10% and 20% noise was added to the original Bunny point cloud, then the modified Helmholtz algorithm, triangulation algorithm, and Poisson algorithm were used for fitting the surface. The simulation experiment results show that the Poisson algorithm has the best robustness. In this paper, these methods are applied to the collected pantograph point cloud data. The experimental results show that when there is noise in the pantograph point cloud , the Poisson reconstruction algorithm can still better restore the overall distribution of the pantograph slide surface wear.
KEYWORDS: Clouds, 3D modeling, Detection and tracking algorithms, Data modeling, Statistical modeling, Remote sensing, Lithium, Laser applications, Ear, 3D acquisition
With the increasing resolution of 3D laser scanners, the number of points in 3D point cloud becoming huge. For example,the number of points in the point cloud that collected form key components of train bottom usually reaches hundreds of thousands to millions. Generally, we would down sample the point cloud before the recognition, registration and reconstruction, which means using a simplified point set with less amount of points to represent the raw data. The Farthest Point Sampling algorithm (FPS) has been proven to be able to traverse the entire point cloud well by uniform sampling. However, it takes a long time for iterative calculation so that the calculation efficiency of this sampling algorithm performed not very well when the amount of points is large, whether it is used for deep learning or using handcraft algorithms for processing, it takes too much time in the sampling process. Therefore, this paper purposed a faster algorithm based on FPS that dividing the point cloud into several grids by octree, then allocating the number of sampling points in each grid according to the density of the points in the grids. The experimental results show that its running time is greatly reduced , and the calculation efficiency is significantly improved. Additionally, the results of the sampling points could also traverse the raw data and retain enough features for meanwhile. It can be practically applied to process the Traction rod device, Nut, Bolt and Wheelset or the other 3D point cloud of key components of train bottom definitely , making detection more efficient.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.