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

TL;DR

MapRF introduces a weakly supervised method for online HD map construction using 2D labels and NeRF-guided self-training, achieving near fully supervised performance without costly 3D annotations.

Contribution

The paper presents a novel NeRF-based framework that constructs 3D maps from 2D labels, reducing annotation costs and improving scalability for autonomous driving.

Findings

Achieves around 75% of fully supervised baseline performance.

Outperforms several 2D label-based approaches.

Demonstrates effectiveness on Argoverse 2 and nuScenes datasets.

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.