ELiTe: Efficient Image-to-LiDAR Knowledge Transfer for Semantic Segmentation

• URL: http://arxiv.org/abs/2405.04121v1
• Date: Tue, 7 May 2024 08:44:13 GMT
• Title: ELiTe: Efficient Image-to-LiDAR Knowledge Transfer for Semantic Segmentation
• Authors: Zhibo Zhang, Ximing Yang, Weizhong Zhang, Cheng Jin,
• Abstract summary: Cross-modal knowledge transfer enhances point cloud representation learning in LiDAR semantic segmentation. Despite its potential, the textitweak teacher challenge arises due to repetitive and non-diverse car camera images and sparse, inaccurate ground truth labels. We propose the Efficient Image-to-LiDAR Knowledge Transfer (ELiTe) paradigm to address this problem.
• Score: 15.404188754049317
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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