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Prototype Enhancement for Few-Shot Point Cloud Semantic Segmentation

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Algorithms and Architectures for Parallel Processing (ICA3PP 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15252))

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Abstract

Few-shot point cloud semantic segmentation plays a fundamental role in the computer vision community since annotating point cloud data is quite time-consuming and labor-intensive. Current semantic segmentation methods employ few-shot learning to reduce dependence on labeled samples and enhance model generalization to new categories. Due to the complex 3D geometries of point clouds, significant feature variations exist even within the same category, meaning that a few training samples (support set) might not fully capture all category features. This discrepancy leads to differences in distribution between the support set and the samples used to evaluate the model (query set), impacting the effectiveness of traditional semantic segmentation approaches. In our paper, we employ a prototype enhancement strategy for few-shot point cloud semantic segmentation. Specifically, to align the prototype representation from the support set more closely with the query set, our framework proposes two modules to enhance the generated original prototype, we have developed a Cross Feature Enhancement module, which enhances support set features by reducing differences in terms of distribution of support and query sets. Moreover, we proposed a prototype correction module to refine the prototypes with the aim of matching query sets accurately. We conducted thorough experiments demonstrates the state-of-the-art performance of our model on publicly available benchmarks including S3DIS and ScanNet.

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Acknowledgement

This work was supported in part by the National Natural Science Foundation of Jiangsu Province under Grant BK20221379; in part by the CNPC-CZU Innovation Alliance, Changzhou University, under Grant CCIA2023-01; and in part by the Changzhou Leading Innovative Talent Introduction & Cultivation Project 20221460. It is also supported by the Science and Technology Development Fund, Macao SAR under Grant 0004/2023/ITP1.

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Authors and Affiliations

  1. Changzhou University, Changzhou, 213164, China

    Zhengyao Li, Gengshen Wu & Yi Liu

  2. City University of Macau, Macau, 999078, China

    Zhengyao Li & Yi Liu

Authors
  1. Zhengyao Li
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  2. Gengshen Wu
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  3. Yi Liu
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Correspondence to Yi Liu .

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Editors and Affiliations

  1. City University of Macau, Macau, China

    Tianqing Zhu

  2. Guangzhou University, Guangzhou, China

    Jin Li

  3. University of Salerno, Fisciano, Italy

    Aniello Castiglione

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Li, Z., Wu, G., Liu, Y. (2025). Prototype Enhancement for Few-Shot Point Cloud Semantic Segmentation. In: Zhu, T., Li, J., Castiglione, A. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2024. Lecture Notes in Computer Science, vol 15252. Springer, Singapore. https://doi.org/10.1007/978-981-96-1528-5_18

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  • DOI: https://doi.org/10.1007/978-981-96-1528-5_18

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