{"ID":2877984,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.18788","arxiv_id":"2508.18788","title":"PseudoMapTrainer: Learning Online Mapping without HD Maps","abstract":"Online mapping models show remarkable results in predicting vectorized maps from multi-view camera images only. However, all existing approaches still rely on ground-truth high-definition maps during training, which are expensive to obtain and often not geographically diverse enough for reliable generalization. In this work, we propose PseudoMapTrainer, a novel approach to online mapping that uses pseudo-labels generated from unlabeled sensor data. We derive those pseudo-labels by reconstructing the road surface from multi-camera imagery using Gaussian splatting and semantics of a pre-trained 2D segmentation network. In addition, we introduce a mask-aware assignment algorithm and loss function to handle partially masked pseudo-labels, allowing for the first time the training of online mapping models without any ground-truth maps. Furthermore, our pseudo-labels can be effectively used to pre-train an online model in a semi-supervised manner to leverage large-scale unlabeled crowdsourced data. The code is available at github.com/boschresearch/PseudoMapTrainer.","short_abstract":"Online mapping models show remarkable results in predicting vectorized maps from multi-view camera images only. However, all existing approaches still rely on ground-truth high-definition maps during training, which are expensive to obtain and often not geographically diverse enough for reliable generalization. In this...","url_abs":"https://arxiv.org/abs/2508.18788","url_pdf":"https://arxiv.org/pdf/2508.18788v1","authors":"[\"Christian Löwens\",\"Thorben Funke\",\"Jingchao Xie\",\"Alexandru Paul Condurache\"]","published":"2025-08-26T08:13:30Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.LG\",\"cs.RO\"]","methods":"[]","has_code":false}