A self-adaptive segmentation method for a point cloud.

Fan, Y., Wang, M., Geng, N., He, D., Chang, J. and Zhang, J. J., 2017. A self-adaptive segmentation method for a point cloud. Visual Computer, 1 - 15. (In Press)

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DOI: 10.1007/s00371-017-1405-6

Abstract

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%.

Item Type:Article
ISSN:0178-2789
Uncontrolled Keywords:Point cloud; Segmentation; Seed point; Region growing
Group:Faculty of Science & Technology
ID Code:29337
Deposited By: Unnamed user with email symplectic@symplectic
Deposited On:12 Jun 2017 13:40
Last Modified:12 Jun 2017 13:40

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