Cross-modal knowledge transfer enhances point cloud representation learning
in LiDAR semantic segmentation. Despite its potential, the \textit{weak teacher
challenge} arises due to repetitive and non-diverse car camera images and
sparse, inaccurate ground truth labels. To address this, we propose the
Efficient Image-to-LiDAR Knowledge Transfer (ELiTe) paradigm. ELiTe introduces
Patch-to-Point Multi-Stage Knowledge Distillation, transferring comprehensive
knowledge from the Vision Foundation Model (VFM), extensively trained on
diverse open-world images. This enables effective knowledge transfer to a
lightweight student model across modalities. ELiTe employs Parameter-Efficient
Fine-Tuning to strengthen the VFM teacher and expedite large-scale model
training with minimal costs. Additionally, we introduce the Segment Anything
Model based Pseudo-Label Generation approach to enhance low-quality image
labels, facilitating robust semantic representations. Efficient knowledge
transfer in ELiTe yields state-of-the-art results on the SemanticKITTI
benchmark, outperforming real-time inference models. Our approach achieves this
with significantly fewer parameters, confirming its effectiveness and
efficiency.

通过跨模态知识转移增强了点云表示学习在激光雷达语义分割中的应用，引入有效的知识转移策略和众多优化方法，得到了领先的性能并且具有较低的模型参数。