GSMap: 2D Gaussians for Online HD Mapping
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866918505232728064 |
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| author | Zeng, Zhenxuan Wang, Lingxuan Yang, Sheng He, Yanan Chen, Mingxia Suo, Wei Wang, Peng |
| author_facet | Zeng, Zhenxuan Wang, Lingxuan Yang, Sheng He, Yanan Chen, Mingxia Suo, Wei Wang, Peng |
| contents | Accurate High-Definition (HD) map construction is critical for autonomous driving, yet existing methods face a fundamental trade-off: vectorization-based approaches preserve topology but struggle with geometric fidelity, while rasterization-based approaches enable precise geometric supervision but produce unstructured outputs. To bridge this gap, we propose GSMap, a novel framework that unifies both paradigms via a learnable 2D Gaussian representation. Each map element is modeled as an ordered sequence of 2D Gaussians, whose centers correspond to the vertices of the vectorized polyline/polygon. This formulation enables simultaneous optimization through: (1) Differentiable rasterization that enforces pixel-level geometric constraints, and (2) Topology-aware vectorization that maintains structural regularity. Experiments on both nuScenes and Argoverse2 demonstrate that our Gaussian-based representation effectively unifies geometric and topological learning, achieving significant performance improvements and demonstrating strong compatibility with existing HD mapping architectures. Code will be available at https://github.com/peakpang/GSMap |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_09619 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | GSMap: 2D Gaussians for Online HD Mapping Zeng, Zhenxuan Wang, Lingxuan Yang, Sheng He, Yanan Chen, Mingxia Suo, Wei Wang, Peng Computer Vision and Pattern Recognition Accurate High-Definition (HD) map construction is critical for autonomous driving, yet existing methods face a fundamental trade-off: vectorization-based approaches preserve topology but struggle with geometric fidelity, while rasterization-based approaches enable precise geometric supervision but produce unstructured outputs. To bridge this gap, we propose GSMap, a novel framework that unifies both paradigms via a learnable 2D Gaussian representation. Each map element is modeled as an ordered sequence of 2D Gaussians, whose centers correspond to the vertices of the vectorized polyline/polygon. This formulation enables simultaneous optimization through: (1) Differentiable rasterization that enforces pixel-level geometric constraints, and (2) Topology-aware vectorization that maintains structural regularity. Experiments on both nuScenes and Argoverse2 demonstrate that our Gaussian-based representation effectively unifies geometric and topological learning, achieving significant performance improvements and demonstrating strong compatibility with existing HD mapping architectures. Code will be available at https://github.com/peakpang/GSMap |
| title | GSMap: 2D Gaussians for Online HD Mapping |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2605.09619 |