High-resolution maps are a crucial factor for the functionality of highly automated driving functions. These contain precise information about lane geometry, road conditions and traffic signs. Although the importance of high-definition maps, creating these is still an ongoing research topic. While the extraction of road geometry and lane information has seen significant progress, the reconstruction of traffic signs is in its early stages. We propose a novel approach for the accurate 3D localization and reconstruction of directional traffic signs, based on monocular images. For a flexible and extensible process, we use a neural radiance field based reconstruction pipeline. We demonstrate that our approach can localize signs with high precision, outperforming current methods, while also generating 3D representations, for use in mapping or simulation applications.
A NeRF-Based Approach for Monocular Traffic Sign Reconstruction and Localization
2024-09-24
2333831 byte
Conference paper
Electronic Resource
English
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