GSMap: 2D Gaussians for Online HD Mapping

2026-05-10

AI Summary

Existing high-definition map construction methods struggle to simultaneously achieve geometric accuracy and topological correctness: vectorization-based approaches preserve structural integrity but suffer from geometric distortions, whereas rasterization-based methods offer precise geometry yet lack explicit structural representation. To address this limitation, this work proposes GSMap, a novel framework that introduces learnable 2D Gaussian sequences to represent map elements, modeling vector vertices as Gaussian centers. By integrating differentiable rasterization for pixel-level geometric constraints and topology-aware vectorization to enforce structural regularity, GSMap enables end-to-end joint optimization of geometry and topology. The method significantly outperforms existing approaches on both nuScenes and Argoverse2 benchmarks while remaining compatible with mainstream HD map architectures.

Abstract

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

Problem

• HD mapping
• vectorization
• rasterization
• geometric fidelity
• topology

Innovation

• 2D Gaussians
• HD mapping
• differentiable rasterization
• topology-aware vectorization
• autonomous driving