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Pseudo-embedding for Generalized Few-Shot 3D Segmentation

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Computer Vision – ECCV 2024 (ECCV 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15094))

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Abstract

Existing generalized few-shot 3D segmentation (GFS3DS) methods typically prioritize enhancing the training of base-class prototypes while neglecting the rich semantic information within background regions for future novel classes. We introduce a novel GFS3DS learner that strategically leverages background context to improve both base prototype training and few-shot adaptability. Our method employs foundation models to extract semantic features from background points and grounds on text embeddings to cluster background points into pseudo-classes. This approach facilitates clearer base/novel class differentiation and generates pseudo prototypes that effectively mimic novel support samples. Comprehensive experiments on S3DIS and ScanNet datasets demonstrate the state-of-the-art performance of our method in both 1-shot and 5-shot tasks. Our approach significantly advances GFS3DS by unlocking the potential of background context, offering a promising avenue for broader applications. Code: https://github.com/jimtsai23/PseudoEmbed.

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Acknowledgements

This work was supported in part by the NSTC grants 111-2221-E-001-011-MY2, 112-2634-F-007-002 and 112-2221-E-A49-100-MY3 of Taiwan. We are grateful to the National Center for High-performance Computing for providing computational resources and facilities.

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Authors and Affiliations

  1. Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan

    Chih-Jung Tsai & Hwann-Tzong Chen

  2. Aeolus Robotics, Hsinchu, Taiwan

    Chih-Jung Tsai & Hwann-Tzong Chen

  3. Institute of Information Science, Academia Sinica, Taipei, Taiwan

    Tyng-Luh Liu

Authors
  1. Chih-Jung Tsai
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  2. Hwann-Tzong Chen
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Corresponding author

Correspondence to Hwann-Tzong Chen .

Editor information

Editors and Affiliations

  1. University of Birmingham, Birmingham, UK

    Aleš Leonardis

  2. University of Trento, Trento, Italy

    Elisa Ricci

  3. Technical University of Darmstadt, Darmstadt, Germany

    Stefan Roth

  4. Princeton University, Princeton, NJ, USA

    Olga Russakovsky

  5. Czech Technical University in Prague, Prague, Czech Republic

    Torsten Sattler

  6. École des Ponts ParisTech, Marne-la-Vallée, France

    Gül Varol

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Tsai, CJ., Chen, HT., Liu, TL. (2025). Pseudo-embedding for Generalized Few-Shot 3D Segmentation. In: Leonardis, A., Ricci, E., Roth, S., Russakovsky, O., Sattler, T., Varol, G. (eds) Computer Vision – ECCV 2024. ECCV 2024. Lecture Notes in Computer Science, vol 15094. Springer, Cham. https://doi.org/10.1007/978-3-031-72764-1_22

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