LiDAR point cloud segmentation is one of the most fundamental tasks for
autonomous driving scene understanding. However, it is difficult for existing
models to achieve both high inference speed and accuracy simultaneously. For
example, voxel-based methods perform well in accuracy, while Bird's-Eye-View
(BEV)-based methods can achieve real-time inference. To overcome this issue, we
develop an effective 3D-to-BEV knowledge distillation method that transfers
rich knowledge from 3D voxel-based models to BEV-based models. Our framework
mainly consists of two modules: the voxel-to-pillar distillation module and the
label-weight distillation module. Voxel-to-pillar distillation distills sparse
3D features to BEV features for middle layers to make the BEV-based model aware
of more structural and geometric information. Label-weight distillation helps
the model pay more attention to regions with more height information. Finally,
we conduct experiments on the SemanticKITTI dataset and Paris-Lille-3D. The
results on SemanticKITTI show more than 5% improvement on the test set,
especially for classes such as motorcycle and person, with more than 15%
improvement. The code can be accessed at
this https URL

该研究提出了一种有效的三维（3D）到 Bird's-Eye-View（BEV）知识蒸馏方法，将丰富的知识从基于 Voxel 的模型传递到基于 BEV 的模型，该方法应用于自动驾驶领域的 LiDAR 点云分割任务中，明显提高了准确性，SemanticKITTI 数据集是该研究的实验验证平台。