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.

翻译：在自动驾驶中，新颖物体和标注缺失对基于深度学习的传统3D激光雷达语义分割构成了挑战。小样本学习是解决这些问题的可行途径。然而，当前的小样本语义分割方法聚焦于相机数据，且大多仅预测新类别而未考虑基类。出于安全考虑，该设置无法直接应用于自动驾驶。因此，我们提出一种同时预测新类别与基类的小样本3D激光雷达语义分割方法。该方法旨在解决广义小样本语义分割中的背景模糊性问题。我们首先回顾了原始的交叉熵与知识蒸馏损失，随后提出一种融合背景信息的新损失函数，以实现3D激光雷达小样本语义分割。在SemanticKITTI数据集上的大量实验验证了本方法的有效性。