Mapping like a Skeptic: Probabilistic BEV Projection for Online HD Mapping


Fatih Erdoğan (Koç University), Merve Rabia Barin (Koç University), Fatma Güney (Koç University)
The 35th British Machine Vision Conference
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Abstract

Constructing high-definition (HD) maps from sensory input requires accurately map- ping the road elements in image space to the Bird’s Eye View (BEV) space. The pre- cision of this mapping directly impacts the quality of the final vectorized HD map. Existing HD mapping approaches outsource the projection to standard mapping tech- niques, such as attention-based ones. However, these methods struggle with accuracy due to generalization problems, often hallucinating non-existent road elements. Our key idea is to start with a geometric mapping based on camera parameters and adapt it to the scene to extract relevant map information from camera images. To implement this, we propose a novel probabilistic projection mechanism with confidence scores to (i) refine the mapping to better align with the scene and (ii) filter out irrelevant ele- ments that should not influence HD map generation. In addition, we improve tempo- ral processing by using confidence scores to selectively accumulate reliable informa- tion over time. Experiments on new splits of the nuScenes and Argoverse2 datasets demonstrate improved performance over state-of-the-art approaches, indicating better generalization. The improvements are particularly pronounced on nuScenes and in the challenging long perception range. Our code and model checkpoints are available at https://github.com/Fatih-Erdogan/mapping-like-skeptic.

Citation

@inproceedings{Erdoğan_2025_BMVC,
author    = {Fatih Erdoğan and Merve Rabia Barin and Fatma Güney},
title     = {Mapping like a Skeptic: Probabilistic BEV Projection for Online HD Mapping},
booktitle = {36th British Machine Vision Conference 2025, {BMVC} 2025, Sheffield, UK, November 24-27, 2025},
publisher = {BMVA},
year      = {2025},
url       = {https://bmva-archive.org.uk/bmvc/2025/assets/papers/Paper_745/paper.pdf}
}


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