仅用激光雷达构建高精地图，通过在线知识蒸馏提升语义分割精度。
LIE: LiDAR-only HD Map Construction with Intensity Enhancement via Online Knowledge Distillation
- 利用激光雷达与强度图融合，通过在线知识蒸馏提供密集监督信号。
- 在nuScenes上达到8.2%更高的mIoU，超越当前最优单模态方法。
- 适用于远距离及恶劣天气，少量微调即可适配新数据集。
车载高精地图构建是自动驾驶的关键。现有方法依赖多视角摄像头实现低成本语义分割，但摄像头缺乏深度信息，难以准确还原场景几何结构；而激光雷达虽能提供精确的3D测量，却缺少丰富的语义和纹理线索。本文提出LIE——一种仅使用激光雷达的语义地图构建方法，采用在线知识蒸馏机制弥补语义缺失。具体而言，教师分支融合学生端的激光雷达特征与对应2D强度图块，为地图元素分割提供密集监督。实验表明，该方法在nuScenes数据集上比最先进的基于相机的方法高出8.2%的mIoU，且在长距离、复杂天气与光照条件下表现稳健。仅用10%的数据微调即可高效适配Argoverse2，性能超过在全量数据上训练的相机模型。源代码将公开。
原文摘要 · Abstract (English)
Online High-Definition (HD) map construction is a key component of autonomous driving. Recent methods rely on multi-view camera images for cost-effective HD map segmentation, but cameras lack depth information for accurate scene geometry. In contrast, LiDAR provides precise 3D measurements but lacks dense semantic cues. In this work, we propose LIE, LiDAR-only semantic map construction method that employ Knowledge Distillation (KD) to handle the lack of dense semantic and texture cues. Specifically, the teacher branch fuses student LiDAR features and the corresponding 2D intensity map tile to provide dense supervision for segmenting map elements using online distillation scheme. Experimental results show that our method outperforms all single-modality approaches, achieving 8.2% higher mIoU than the state-of-the-art camera-based model on nuScenes. LIE is robust over long ranges and under challenging weather and lighting, and efficiently adapts to Argoverse2 with only 10% fine-tuning, surpassing camera-based models trained on the full dataset. Source code will be available \href{https://iv.ee.hm.edu/lie/}{here}.
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