MapRF: 通过NeRF引导的自训练实现弱监督在线HD地图构建
MapRF: Weakly Supervised Online HD Map Construction via NeRF-Guided Self-Training
AI总结：
MapRF通过NeRF引导的自训练方法，利用2D图像标签实现弱监督在线HD地图构建，实现了与全监督方法相当的性能。
摘要：
自动驾驶系统受益于提供道路基础设施关键信息的高精度（HD）地图。在线构建HD地图提供了一种可扩展的方法，从车载传感器生成局部地图。然而，现有方法通常依赖于成本高昂的3D地图注释进行训练，这限制了它们在多样化驾驶环境中的泛化能力和可扩展性。在本工作中，我们提出了MapRF，一种弱监督框架，仅使用2D图像标签学习构建3D地图。为了生成高质量的伪标签，我们引入了一个新的神经辐射场（NeRF）模块，该模块基于地图预测进行条件设置，以重建视图一致的3D几何和语义。这些伪标签随后被迭代用于改进地图网络，以自训练的方式进行逐步改进，无需额外监督。此外，为了减轻自训练过程中的误差累积，我们提出了一种地图到射线匹配策略，将地图预测与从2D标签导出的相机射线对齐。在Argoverse 2和nuScenes数据集上的广泛实验表明，MapRF在性能上与完全监督的方法相当，达到了基线的约75%，并且在仅使用2D标签的情况下超过了几种方法。这突显了MapRF在自动驾驶中实现可扩展且低成本在线HD地图构建的潜力。
英文摘要：
Autonomous driving systems benefit from high-definition (HD) maps that provide critical information about road infrastructure. The online construction of HD maps offers a scalable approach to generate local maps from on-board sensors. However, existing methods typically rely on costly 3D map annotations for training, which limits their generalization and scalability across diverse driving environments. In this work, we propose MapRF, a weakly supervised framework that learns to construct 3D maps using only 2D image labels. To generate high-quality pseudo labels, we introduce a novel Neural Radiance Fields (NeRF) module conditioned on map predictions, which reconstructs view-consistent 3D geometry and semantics. These pseudo labels are then iteratively used to refine the map network in a self-training manner, enabling progressive improvement without additional supervision. Furthermore, to mitigate error accumulation during self-training, we propose a Map-to-Ray Matching strategy that aligns map predictions with camera rays derived from 2D labels. Extensive experiments on the Argoverse 2 and nuScenes datasets demonstrate that MapRF achieves performance comparable to fully supervised methods, attaining around 75% of the baseline while surpassing several approaches using only 2D labels. This highlights the potential of MapRF to enable scalable and cost-effective online HD map construction for autonomous driving.
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