A self-adaptive segmentation method for a point cloud

This source preferred by Jian Jun Zhang and Jian Chang

This data was imported from Scopus:

Authors: Fan, Y., Wang, M., Geng, N., He, D., Chang, J. and Zhang, J.J.

http://eprints.bournemouth.ac.uk/30124/

Journal: Visual Computer

Pages: 1-15

ISSN: 0178-2789

DOI: 10.1007/s00371-017-1405-6

© 2017 Springer-Verlag Berlin Heidelberg The segmentation of a point cloud is one of the key technologies for three-dimensional reconstruction, and the segmentation from three-dimensional views can facilitate reverse engineering. In this paper, we propose a self-adaptive segmentation algorithm, which can address challenges related to the region-growing algorithm, such as inconsistent or excessive segmentation. Our algorithm consists of two main steps: automatic selection of seed points according to extracted features and segmentation of the points using an improved region-growing algorithm. The benefits of our approach are the ability to select seed points without user intervention and the reduction of the influence of noise. We demonstrate the robustness and effectiveness of our algorithm on different point cloud models and the results show that the segmentation accuracy rate achieves 96%.

The data on this page was last updated at 04:48 on February 24, 2018.