Generalized Few-shot 3D Point Cloud Segmentation with Vision-Language
  Model
Authors: name: Zhaochong An, name: Guolei Sun, name: Yun Liu, name: Runjia Li, name: Junlin Han, name: Ender Konukoglu, name: Serge Belongie

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