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A self-adaptive segmentation method for a point cloud

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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%.

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Acknowledgements

This work was partially funded by the National High-tech research and Development Program (863 Program: 2013AA10230402), National Natural Science Foundation of China (61402374), and the China Postdoctoral Science Foundation (2014M562457). The authors acknowledge the authors of [26], Shenzhen Key Lab of Visual Computing and Visual Analytics for the source data and the models.

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Authors and Affiliations

  1. College of Information Engineering, Northwest A&F University, Xianyang, China

    Yuling Fan, Meili Wang & Nan Geng

  2. College of Mechanical and Electronic Engineering, Northwest A&F University, Xianyang, China

    Dongjian He

  3. National Centre for Computer Animation, Media School, Bournemouth University, Bournemouth, UK

    Jian Chang & Jian J. Zhang

Authors
  1. Yuling Fan
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  2. Meili Wang
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  3. Nan Geng
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  4. Dongjian He
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  5. Jian Chang
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  6. Jian J. Zhang
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Corresponding author

Correspondence to Dongjian He.

Additional information

Yuling Fan and Meili Wang are the co-first authors.

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Fan, Y., Wang, M., Geng, N. et al. A self-adaptive segmentation method for a point cloud. Vis Comput 34, 659–673 (2018). https://doi.org/10.1007/s00371-017-1405-6

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