|
Digital Library of the
European Council for Modelling and Simulation |
Title: |
Discrete Point Cloud Filtering
And Searching Based On VGSO Algorithm |
Authors: |
Fengjun Hu, Yanwei Zhao, Wanliang Wang, Xianping Huang |
Published in: |
(2013).ECMS 2013 Proceedings edited
by: W. Rekdalsbakken, R. T. Bye, H. Zhang European Council for Modeling
and Simulation. doi:10.7148/2013 ISBN:
978-0-9564944-6-7 27th
European Conference on Modelling and Simulation, Aalesund, Norway, May 27th –
30th, 2013 |
Citation
format: |
Fengjun Hu,
Yanwei Zhao, Wanliang
Wang, Xianping Huang (2013). Discrete Point Cloud
Filtering And Searching Based On VGSO Algorithm,
ECMS 2013 Proceedings edited by: W. Rekdalsbakken, R. T. Bye, H. Zhang, European Council for Modeling
and Simulation. doi:10.7148/2013-0850 |
DOI: |
http://dx.doi.org/10.7148/2013-0850 |
Abstract: |
The
massive point cloud data obtained through the computer vision is uneven in
density together with a lot of noise and outliers, which will greatly reduce
the point cloud search efficiency and affect the surface reconstruction.
Based on that, this paper presents a filtering algorithm based on Voxel Grid Statistical Outlier (VGSO): Firstly, 3D voxel grid is created for the massive point cloud data
approximating other points inside the voxel with
the centroid of all points; then, the neighborhood
of discrete points is analyzed statistically, calculating average distance of
every point to its neighboring points and filtering the outliers outside the
reference ranges of average distance from the data set; finally, the
segmentation rules are improved according to the characteristics of KD-Tree. A
large amount of experimental results show that this stable and reliable
algorithm can compress and filter the point cloud data quickly and effectively.
At the same time, it greatly accelerates the search speed. |
Full
text: |