无需高精地图即可训练在线地图模型，靠伪标签实现自监督学习。
PseudoMapTrainer: Learning Online Mapping without HD Maps
- 用多视角图像和高斯点云重建路面，结合语义分割生成伪标签。
- 提出掩码感知分配算法与损失函数，支持部分遮挡的伪标签训练。
- 首次实现无真值地图的端到端在线地图训练，适合自动驾驶数据闭环场景。
在线地图模型仅通过多视角摄像头图像即可预测矢量化地图，但现有方法仍依赖昂贵且地理分布有限的高精地图进行训练。本文提出PseudoMapTrainer，利用未标注传感器数据生成伪标签：通过高斯点云重构道路表面，并结合预训练2D分割网络的语义信息。同时引入掩码感知的分配算法与损失函数，有效处理部分遮挡的伪标签，首次实现无需任何真值地图的在线地图模型训练。此外，该伪标签可高效用于半监督预训练，以利用大规模未标注众包数据。代码已开源。
原文摘要 · Abstract (English)
Online mapping models show remarkable results in predicting vectorized maps from multi-view camera images only. However, all existing approaches still rely on ground-truth high-definition maps during training, which are expensive to obtain and often not geographically diverse enough for reliable generalization. In this work, we propose PseudoMapTrainer, a novel approach to online mapping that uses pseudo-labels generated from unlabeled sensor data. We derive those pseudo-labels by reconstructing the road surface from multi-camera imagery using Gaussian splatting and semantics of a pre-trained 2D segmentation network. In addition, we introduce a mask-aware assignment algorithm and loss function to handle partially masked pseudo-labels, allowing for the first time the training of online mapping models without any ground-truth maps. Furthermore, our pseudo-labels can be effectively used to pre-train an online model in a semi-supervised manner to leverage large-scale unlabeled crowdsourced data. The code is available at github.com/boschresearch/PseudoMapTrainer.
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