RoGS: Adaptive Meshgrid Gaussian for Large-Scale Road Surface Mapping
Computer Vision and Pattern Recognition
Summary
The authors developed a new method called ROADGS-T to create better and faster maps of road surfaces for self-driving cars. Their technique uses small 2D Gaussian shapes arranged on a grid to represent road features like colors and shapes more accurately and efficiently than older methods. They also make the grid smarter by placing more details in important areas like lane markings and edges, while keeping simple sections less crowded. Additionally, they improve the mapping by using information from multiple nearby vehicle positions to make the road surface estimate more reliable.
Authors
Tianchen Deng, Zhiheng Feng, Wenhua Wu, Ziming Li, Siting Zhu, Hesheng Wang
Abstract
Road surface mapping plays a crucial role in autonomous driving, supporting high-definition map generation, lane-level perception, and automatic road annotation. Recent mesh-based road surface reconstruction methods have shown promising results, but they still suffer from limited reconstruction quality and high optimization cost, especially in large-scale driving scenarios. To address these limitations, we propose ROADGS-T, a robust and efficient large-scale road surface mapping framework based on adaptive meshgrid Gaussian representation. Specifically, we model the road surface by placing 2D Gaussian surfels on a meshgrid, where each surfel explicitly stores color, semantic, and geometric information. Compared with conventional mesh-based representations and 3D Gaussian primitives, the proposed meshgrid Gaussian representation better matches the thin-surface property of roads while significantly reducing redundant primitives and overlap during optimization. To further improve representation efficiency and structural fidelity, we introduce a road-structure-aware adaptive meshgrid strategy, which allocates denser Gaussian surfels to geometrically or semantically complex regions, such as lane markings, road boundaries, and height discontinuities, while maintaining a compact representation in flat road areas. Moreover, instead of relying on a single nearest vehicle pose, we design a trajectory-consistency-guided pose-robust refinement strategy, which estimates local surface priors from multiple neighboring poses and adaptively weights pose-guided height regularization according to their geometric consistency.