GSMap: 2D Gaussians for Online HD Mapping
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 th...
| Authors | Zhenxuan Zeng, Lingxuan Wang, Sheng Yang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 10 May 2026 |
| Last updated at source | 16 May 2026 |
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