RelMap: Enhancing Online Map Construction with Class-Aware Spatial Relation and Semantic Priors
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866915514183319552 |
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| author | Cai, Tianhui Zhang, Yun Zhou, Zewei Huang, Zhiyu Ma, Jiaqi |
| author_facet | Cai, Tianhui Zhang, Yun Zhou, Zewei Huang, Zhiyu Ma, Jiaqi |
| contents | Online high-definition (HD) map construction is crucial for scaling autonomous driving systems. While Transformer-based methods have become prevalent in online HD map construction, most existing approaches overlook the inherent spatial dependencies and semantic relationships among map elements, which constrains their accuracy and generalization capabilities. To address this, we propose RelMap, an end-to-end framework that explicitly models both spatial relations and semantic priors to enhance online HD map construction. Specifically, we introduce a Class-aware Spatial Relation Prior, which explicitly encodes relative positional dependencies between map elements using a learnable class-aware relation encoder. Additionally, we design a Mixture-of-Experts-based Semantic Prior, which routes features to class-specific experts based on predicted class probabilities, refining instance feature decoding. RelMap is compatible with both single-frame and temporal perception backbones, achieving state-of-the-art performance on both the nuScenes and Argoverse 2 datasets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_21567 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | RelMap: Enhancing Online Map Construction with Class-Aware Spatial Relation and Semantic Priors Cai, Tianhui Zhang, Yun Zhou, Zewei Huang, Zhiyu Ma, Jiaqi Computer Vision and Pattern Recognition Online high-definition (HD) map construction is crucial for scaling autonomous driving systems. While Transformer-based methods have become prevalent in online HD map construction, most existing approaches overlook the inherent spatial dependencies and semantic relationships among map elements, which constrains their accuracy and generalization capabilities. To address this, we propose RelMap, an end-to-end framework that explicitly models both spatial relations and semantic priors to enhance online HD map construction. Specifically, we introduce a Class-aware Spatial Relation Prior, which explicitly encodes relative positional dependencies between map elements using a learnable class-aware relation encoder. Additionally, we design a Mixture-of-Experts-based Semantic Prior, which routes features to class-specific experts based on predicted class probabilities, refining instance feature decoding. RelMap is compatible with both single-frame and temporal perception backbones, achieving state-of-the-art performance on both the nuScenes and Argoverse 2 datasets. |
| title | RelMap: Enhancing Online Map Construction with Class-Aware Spatial Relation and Semantic Priors |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2507.21567 |