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Feature extraction of point clouds based on region clustering segmentation

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Abstract

This paper proposes a feature extraction method for scattered point clouds. First, a clustering algorithm is used to divide point clouds into different regions that represent the original features. In each sub-region, we calculate the angles between the directed line segments from sampling points to the neighborhood points and set the angle threshold to identify edge feature points of uniform distribution. For the edge points of non-uniform distribution, we introduce the local neighborhood size as a discrete scale parameter for edge point detection, and then accurately identify and record the detected edge points. Then, according to the mean curvature of point clouds, the local feature weights of sampling points in the sub-region are calculated so that potential sharp feature points in a local area are detected. Finally, a minimum spanning tree of feature points is established to construct connected regions and generate feature point sets. A Bidirectional Principal Component Analysis (BD-PCA) search method is used to trim and break the small branches and multiline segments to generate feature curves. We carried out experiments on point cloud models with different densities to verify the effectiveness and superiority of our method. Results show that the edge features and sharp features are effectively extracted, and our method is not affected by the noise, neighborhood scale, or quality of sampling.

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Funding

This study was funded by the National Natural Science Foundation of China (Nos. 51,365,037 and 51,065,021).

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Authors and Affiliations

  1. School of Architectural and Mechanical Engineering, Chifeng University, Chifeng, 024000, China

    XiaoHui Wang

  2. School of Mechatronic Engineering, Nanchang University, Nanchang, 330031, China

    XiaoHui Wang & LuShen Wu

  3. School of Mechanical and Electrical Engineering, Guizhou Normal University, Guiyang, 550025, China

    HuaWei Chen

Authors
  1. XiaoHui Wang
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  2. HuaWei Chen
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  3. LuShen Wu
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Correspondence to XiaoHui Wang.

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Appendices

Appendix 1

The important parameters of FCM clustering algorithm are as follows: c is the number of clusters. Let the date set X is divided into c(2 ≤ c ≤ n) subsets. P = {p1, p2, ⋯, pc} is the clustering center of these subsets. μik is the membership degree of point. The parameter m is a weighting exponent that controls the level of cluster fuzziness. We can take m = 2 according to one previous work(Pal N R, Bezdek J C (1995) On cluster validity for the fuzzy c-means model. IEEE Transactions on Fuzzy Systems 3(3): 370–379). dik is the squared Euclidean distance, which is used to measure the distance between the feature vector xk of k object and the centerpi of cluster i. The objective function is denoted by Jm.

The following pseudo codes are the details.

figure b

Appendix 2

figure c

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Wang, X., Chen, H. & Wu, L. Feature extraction of point clouds based on region clustering segmentation. Multimed Tools Appl 79, 11861–11889 (2020). https://doi.org/10.1007/s11042-019-08512-1

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  • DOI: https://doi.org/10.1007/s11042-019-08512-1

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