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EEP-Net: Enhancing Local Neighborhood Features and Efficient Semantic Segmentation of Scale Point Clouds

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Pattern Recognition and Computer Vision (PRCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13536))

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Abstract

Semantic segmentation of point clouds at the scene level is a challenging task. Most existing work relies on expensive sampling techniques and tedious pre- and post-processing steps, which are often time-consuming and laborious. To solve this problem, we propose a new module for extracting contextual features from local regions of point clouds, called EEP module in this paper, which converts point clouds from Cartesian coordinates to polar coordinates of local regions, thereby Fade out the geometric representation with rotation invariance in the three directions of XYZ, and the new geometric representation is connected with the position code to form a new spatial representation. It can preserve geometric details and learn local features to a greater extent while improving computational and storage efficiency. This is beneficial for the segmentation task of point clouds. To validate the performance of our method, we conducted experiments on the publicly available standard dataset S3DIS, and the experimental results show that our method achieves competitive results compared to existing methods.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (nos.U21A20487, U1913202, U1813205), CAS Key Technology Talent Program, Shenzhen Technology Project (nos. JSGG20191129094012321, JCYJ20180507182610734)

Author information

Authors and Affiliations

  1. Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China

    Yicheng Liu, Fuxiang Wu, Qieshi Zhang & Jun Chen

  2. Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Beijing, China

    Fuxiang Wu, Qieshi Zhang & Jun Chen

  3. The Chinese University of Hong Kong, Hong Kong, China

    Fuxiang Wu, Qieshi Zhang & Jun Chen

  4. DongGuan University of Technology, Dongguan, China

    Ziliang Ren

Authors
  1. Yicheng Liu
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  2. Fuxiang Wu
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  3. Qieshi Zhang
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  4. Ziliang Ren
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  5. Jun Chen
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Corresponding author

Correspondence to Qieshi Zhang .

Editor information

Editors and Affiliations

  1. Southern University of Science and Technology, Shenzhen, China

    Shiqi Yu

  2. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Zhaoxiang Zhang

  3. Hong Kong Baptist University, Hong Kong, China

    Pong C. Yuen

  4. Northwestern Polytechnical University, Xi'an, China

    Junwei Han

  5. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Tieniu Tan

  6. Hong Kong Baptist University, Hong Kong, China

    Yike Guo

  7. Sun Yat-sen University, Guangzhou, China

    Jianhuang Lai

  8. Southern University of Science and Technology, Shenzhen, China

    Jianguo Zhang

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Liu, Y., Wu, F., Zhang, Q., Ren, Z., Chen, J. (2022). EEP-Net: Enhancing Local Neighborhood Features and Efficient Semantic Segmentation of Scale Point Clouds. In: Yu, S., et al. Pattern Recognition and Computer Vision. PRCV 2022. Lecture Notes in Computer Science, vol 13536. Springer, Cham. https://doi.org/10.1007/978-3-031-18913-5_9

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  • DOI: https://doi.org/10.1007/978-3-031-18913-5_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-18912-8

  • Online ISBN: 978-3-031-18913-5

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