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Waterfall-Net: Waterfall Feature Aggregation for Point Cloud Semantic Segmentation

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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

In this paper, we observe that the point cloud density affects the performance of different categories in 3D point cloud semantic segmentation. Most existing point-based methods implicitly deal with this density issue via extracting multi-scale features in a single forward path. Instead, we propose a Waterfall-Net that explicitly utilizes the density property via cross-connected cascaded sub-networks. In Waterfall-Net, three sub-networks successively process the input point cloud. Each sub-network handles the point features sampled at different densities, obtaining the information at various densities. The output features of one sub-network are up-sampled via a learnable up-sample method and fed into the next sub-network. This Sub-Network Fusing aligns the density of two sub-networks and maintains the contextual information. Meanwhile, Sub-Stage Fusing fuses the sub-stage features between successive sub-networks according to the density. Such waterfall-like feature aggregation ensembles all the features from different densities and enhances the model learning ability. We empirically demonstrate the effectiveness of the Waterfall-Net on two benchmarks. Specifically, it achieves 72.2% mIoU on S3DIS and 55.7% mIoU on SemanticKitti.

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Authors and Affiliations

  1. Engineering Research Center of Digital Forensics, Ministry of Education, School of Computer and Software, Nanjing University of Information Science, Nanjing, 210044, China

    Hui Shuai, Xiang Xu & Qingshan Liu

Authors
  1. Hui Shuai
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  2. Xiang Xu
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  3. Qingshan Liu
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Corresponding author

Correspondence to Hui Shuai .

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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Shuai, H., Xu, X., Liu, Q. (2022). Waterfall-Net: Waterfall Feature Aggregation for Point Cloud Semantic Segmentation. 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_3

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  • DOI: https://doi.org/10.1007/978-3-031-18913-5_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-18912-8

  • Online ISBN: 978-3-031-18913-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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