  In autonomous driving, the novel objects and lack of annotations challenge
the traditional 3D LiDAR semantic segmentation based on deep learning. Few-shot
learning is a feasible way to solve these issues. However, currently few-shot
semantic segmentation methods focus on camera data, and most of them only
predict the novel classes without considering the base classes. This setting
cannot be directly applied to autonomous driving due to safety concerns. Thus,
we propose a few-shot 3D LiDAR semantic segmentation method that predicts both
novel classes and base classes simultaneously. Our method tries to solve the
background ambiguity problem in generalized few-shot semantic segmentation. We
first review the original cross-entropy and knowledge distillation losses, then
propose a new loss function that incorporates the background information to
achieve 3D LiDAR few-shot semantic segmentation. Extensive experiments on
SemanticKITTI demonstrate the effectiveness of our method.
