Density-Based Denoising of Point Cloud


Zaman, Faisal and Wong, Ya Ping and Ng, Boon Yian (2016) Density-Based Denoising of Point Cloud. In: 9th International Conference on Robotic, Vision, Signal Processing and Power Applications. Lecture Notes in Electrical Engineering, 398 . Springer, pp. 287-295. ISBN 978-981-10-1719-3, 978-981-10-1721-6

Full text not available from this repository.


Point cloud source data for surface reconstruction is usually contaminated with noise and outliers. To overcome this deficiency, a density-based point cloud denoising method is presented to remove outliers and noisy points. First, particle-swam optimization technique is employed for automatically approximating optimal bandwidth of multivariate kernel density estimation to ensure the robust performance of density estimation. Then, mean-shift based clustering technique is used to remove outliers through a thresholding scheme. After removing outliers from the point cloud, bilateral mesh filtering is applied to smooth the remaining points. The experimental results show that this approach, comparably, is robust and efficient.

Item Type: Book Section
Uncontrolled Keywords: Swarm intelligence, Point cloud, Denoising, Optimal bandwidth, Particle swarm optimization, Bilateral filter
Subjects: Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions: Faculty of Computing and Informatics (FCI)
Depositing User: Ms Rosnani Abd Wahab
Date Deposited: 29 Nov 2020 10:19
Last Modified: 29 Nov 2020 10:19


Downloads per month over past year

View ItemEdit (login required)