MapRF: Weakly Supervised Online HD Map Construction via NeRF-Guided Self-Training

• URL: http://arxiv.org/abs/2511.19527v1
• Date: Mon, 24 Nov 2025 07:23:10 GMT
• Title: MapRF: Weakly Supervised Online HD Map Construction via NeRF-Guided Self-Training
• Authors: Hongyu Lyu, Thomas Monninger, Julie Stephany Berrio Perez, Mao Shan, Zhenxing Ming, Stewart Worrall,
• Abstract summary: MapRF is a weakly supervised framework that learns to construct 3D maps using only 2D image labels.<n>To mitigate error accumulation during self-training, we propose a Map-to-Ray Matching strategy.
• Score: 6.6099504578472414
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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