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International Conference on Technologies for E-Learning and Digital Entertainment

Edutainment 2017: E-Learning and Games pp 115–137Cite as

3D Point Cloud Classification Based on Discrete Conditional Random Field

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10345))

Abstract

The advantage of the conditional random field (CRF) lies in the construction of the discriminant model and efficient parameter optimization. In the topic of the classification of three-dimensional point cloud, the parameters of the CRF are usually learnt through Gradient Descent and Belief Propagation, in order to optimize the objective energy function of the CRF. These optimization methods do not guarantee the highest global classification accuracy with a high classification accuracy on the smaller classes. In addition, differential features of the point cloud are not sufficiently utilized. In this paper, we use the local geometric shape features to construct the CRF, including the nearest neighbor tetrahedral volume, Gaussian curvature, the neighbourhood normal vector consistency and the neighbourhood minimum principal curvature direction consistency. We propose four discrete criteria for CRF parameter optimization to design the explicit functions, and present concrete solution procedures, in which Monte Carlo method and supervised learning method are employed to estimate the CRF parameters iteratively. Experimental results show that our method can be applied to the classification of the scene of 3D point cloud with plants, especially under the proposed second criterion that maximizing the accuracy with interclass weights. It can be used to improve significantly the classification accuracy of small scale point sets when different classes have great disparity in number.

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Acknowledgements

This work is partly supported by National Natural Science Foundation of China with Nos. 61331018, 61372190, 61501464, 61571439, and 61561003, partly supported by the National High Technology Research and Development Program (863 Program) of China with No. 2015AA016402 and Project 6140001010207.

Author information

Authors and Affiliations

  1. NLPR-LIAMA, Institute of Automation, CAS, Beijing, China

    Xinying Liu, Weiliang Meng, Shiming Xiang & Xiaopeng Zhang

  2. University of Chinese Academy of Sciences, Beijing, China

    Xinying Liu, Weiliang Meng, Shiming Xiang & Xiaopeng Zhang

  3. Beijing Forestry University, Beijing, China

    Hongjun Li

Authors
  1. Xinying Liu
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  2. Hongjun Li
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  3. Weiliang Meng
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  4. Shiming Xiang
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  5. Xiaopeng Zhang
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Corresponding authors

Correspondence to Hongjun Li or Xiaopeng Zhang .

Editor information

Editors and Affiliations

  1. Bournemouth University, Bournemouth, United Kingdom

    Feng Tian

  2. Bournemouth University, Bournemouth, United Kingdom

    Christos Gatzidis

  3. Liverpool John Moores University, Liverpool, United Kingdom

    Abdennour El Rhalibi

  4. Bournemouth University, Bournemouth, United Kingdom

    Wen Tang

  5. Bournemouth University, Bournemouth, United Kingdom

    Fred Charles

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Liu, X., Li, H., Meng, W., Xiang, S., Zhang, X. (2017). 3D Point Cloud Classification Based on Discrete Conditional Random Field. In: Tian, F., Gatzidis, C., El Rhalibi, A., Tang, W., Charles, F. (eds) E-Learning and Games. Edutainment 2017. Lecture Notes in Computer Science(), vol 10345. Springer, Cham. https://doi.org/10.1007/978-3-319-65849-0_14

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  • DOI: https://doi.org/10.1007/978-3-319-65849-0_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-65848-3

  • Online ISBN: 978-3-319-65849-0

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