RoGS: Adaptive Meshgrid Gaussian for Large-Scale Road Surface Mapping

2026-07-16

AI Summary

This work addresses the limitations of existing grid-based road surface reconstruction methods, which suffer from limited reconstruction quality and high optimization costs in large-scale driving scenarios. The authors propose ROADGS-T, a novel framework that represents thin road structures using adaptive mesh-based Gaussian surfels, each encoding color, semantic, and geometric information. By incorporating a road-structure-aware adaptive mesh refinement mechanism and a trajectory-consistency-guided pose-robust optimization strategy, the method enables joint geometric and semantic modeling. This approach significantly improves reconstruction fidelity and structural accuracy while substantially reducing redundant primitives and computational overhead, outperforming both conventional mesh-based and 3D Gaussian methods in large-scale scenes.

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.

Problem

• road surface mapping
• large-scale reconstruction
• mesh-based representation
• optimization cost
• reconstruction quality

Innovation

• adaptive meshgrid Gaussian
• road surface mapping
• Gaussian surfels
• trajectory-consistency-guided refinement
• structure-aware representation