Generalized Few-Shot Point Cloud Segmentation Via Geometric Words
Yating Xu Conghui Hu Na Zhao Gim Hee Lee
Existing fully-supervised point cloud segmentation methods suffer in the
dynamic testing environment with emerging new classes. Few-shot point cloud
segmentation algorithms address this problem by learning to adapt to new
classes at the sacrifice of segmentation accuracy for the base classes, which
severely impedes its practicality. This largely motivates us to present the
first attempt at a more practical paradigm of generalized few-shot point cloud
segmentation, which requires the model to generalize to new categories with
only a few support point clouds and simultaneously retain the capability to
segment base classes. We propose the geometric words to represent geometric
components shared between the base and novel classes, and incorporate them into
a novel geometric-aware semantic representation to facilitate better
generalization to the new classes without forgetting the old ones. Moreover, we
introduce geometric prototypes to guide the segmentation with geometric prior
knowledge. Extensive experiments on S3DIS and ScanNet consistently illustrate
the superior performance of our method over baseline methods. Our code is
available at: https://github.com/Pixie8888/GFS-3DSeg_GWs.