title: Generalized Few-shot 3D Point Cloud Segmentation with Vision-Language
    Model
summary: Generalized few-shot 3D point cloud segmentation (GFS-PCS) adapts models to
  new classes with few support samples while retaining base class segmentation.
  Existing GFS-PCS methods enhance prototypes via interacting with support or
  query features but remain limited by sparse knowledge from few-shot samples.
  Meanwhile, 3D vision-language models (3D VLMs), generalizing across open-world
  novel classes, contain rich but noisy novel class knowledge. In this work, we
  introduce a GFS-PCS framework that synergizes dense but noisy pseudo-labels
  from 3D VLMs with precise yet sparse few-shot samples to maximize the strengths
  of both, named GFS-VL. Specifically, we present a prototype-guided pseudo-label
  selection to filter low-quality regions, followed by an adaptive infilling
  strategy that combines knowledge from pseudo-label contexts and few-shot
  samples to adaptively label the filtered, unlabeled areas. Additionally, we
  design a novel-base mix strategy to embed few-shot samples into training
  scenes, preserving essential context for improved novel class learning.
  Moreover, recognizing the limited diversity in current GFS-PCS benchmarks, we
  introduce two challenging benchmarks with diverse novel classes for
  comprehensive generalization evaluation. Experiments validate the effectiveness
  of our framework across models and datasets. Our approach and benchmarks
  provide a solid foundation for advancing GFS-PCS in the real world. The code is
  at https://github.com/ZhaochongAn/GFS-VL
paper_authors._id: 67e13fcab21a55baf5fc6768
paper_authors.name: Zhaochong An
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-24T12:32:41.985Z
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paper_authors.avatarUrl: /avatars/b6a857e7b725767197dd95bc876f8ad1.svg
paper_authors.fullname: Zhaochong An
paper_authors.type: user
paper_authors.user: ZhaochongAn
paper_authors._id: 67e13fcab21a55baf5fc6769
paper_authors.name: Guolei Sun
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-24T12:55:55.488Z
paper_authors._id: 653c2b3777c2f094527b7a9c
paper_authors.avatarUrl: /avatars/b27ac82a79a2d62cbecda303239efc2c.svg
paper_authors.fullname: Guolei Sun
paper_authors.type: user
paper_authors.user: GuoleiSun
paper_authors._id: 67e13fcab21a55baf5fc676a
paper_authors.name: Yun Liu
paper_authors._id: 67e13fcab21a55baf5fc676b
paper_authors.name: Runjia Li
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-24T12:56:19.776Z
paper_authors._id: 638e29cf319f9c746b87ad4b
paper_authors.avatarUrl: /avatars/70cac8d47847c389eb0393051a64c4a4.svg
paper_authors.fullname: Runjia Li
paper_authors.type: user
paper_authors.user: liguang0115
paper_authors._id: 67e13fcab21a55baf5fc676c
paper_authors.name: Junlin Han
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-24T12:56:41.798Z
paper_authors._id: 636e6ee287545ca5a136b4c3
paper_authors.avatarUrl: /avatars/208d32b1202e2da210146027212dbdd3.svg
paper_authors.fullname: Junlin Han
paper_authors.type: user
paper_authors.user: Junlinh
paper_authors._id: 67e13fcab21a55baf5fc676d
paper_authors.name: Ender Konukoglu
paper_authors._id: 67e13fcab21a55baf5fc676e
paper_authors.name: Serge Belongie
paper_title: Generalized Few-shot 3D Point Cloud Segmentation with Vision-Language
    Model
paper_summary: Generalized few-shot 3D point cloud segmentation (GFS-PCS) adapts models to
  new classes with few support samples while retaining base class segmentation.
  Existing GFS-PCS methods enhance prototypes via interacting with support or
  query features but remain limited by sparse knowledge from few-shot samples.
  Meanwhile, 3D vision-language models (3D VLMs), generalizing across open-world
  novel classes, contain rich but noisy novel class knowledge. In this work, we
  introduce a GFS-PCS framework that synergizes dense but noisy pseudo-labels
  from 3D VLMs with precise yet sparse few-shot samples to maximize the strengths
  of both, named GFS-VL. Specifically, we present a prototype-guided pseudo-label
  selection to filter low-quality regions, followed by an adaptive infilling
  strategy that combines knowledge from pseudo-label contexts and few-shot
  samples to adaptively label the filtered, unlabeled areas. Additionally, we
  design a novel-base mix strategy to embed few-shot samples into training
  scenes, preserving essential context for improved novel class learning.
  Moreover, recognizing the limited diversity in current GFS-PCS benchmarks, we
  introduce two challenging benchmarks with diverse novel classes for
  comprehensive generalization evaluation. Experiments validate the effectiveness
  of our framework across models and datasets. Our approach and benchmarks
  provide a solid foundation for advancing GFS-PCS in the real world. The code is
  at https://github.com/ZhaochongAn/GFS-VL
paper_ai_summary: A generalized few-shot 3D point cloud segmentation framework integrates noisy pseudo-labels from 3D vision-language models with sparse few-shot samples to enhance segmentation accuracy and generalization.
paper_ai_keywords: few-shot 3D point cloud segmentation GFS-PCS 3D vision-language models 3D VLMs prototype-guided pseudo-label selection adaptive infilling strategy novel-base mix strategy benchmarks