Road surface reconstruction plays a crucial role in autonomous driving, which
can be used for road lane perception and autolabeling tasks. Recently,
mesh-based road surface reconstruction algorithms show promising reconstruction
results. However, these mesh-based methods suffer from slow speed and poor
rendering quality. In contrast, the 3D Gaussian Splatting (3DGS) shows superior
rendering speed and quality. Although 3DGS employs explicit Gaussian spheres to
represent the scene, it lacks the ability to directly represent the geometric
information of the scene. To address this limitation, we propose a novel
large-scale road surface reconstruction approach based on 2D Gaussian Splatting
(2DGS), named RoGS. The geometric shape of the road is explicitly represented
using 2D Gaussian surfels, where each surfel stores color, semantics, and
geometric information. Compared to Gaussian spheres, the Gaussian surfels
aligns more closely with the physical reality of the road. Distinct from
previous initialization methods that rely on point clouds for Gaussian spheres,
we introduce a trajectory-based initialization for Gaussian surfels. Thanks to
the explicit representation of the Gaussian surfels and a good initialization,
our method achieves a significant acceleration while improving reconstruction
quality. We achieve excellent results in reconstruction of roads surfaces in a
variety of challenging real-world scenes.

基于 2D 高斯点云散离（2DGS）的 RoGS 方法通过显式地表示道路的几何形状，以及使用基于轨迹的初始化方法，实现了在提升重建质量的同时大幅加速道路表面重建的目标。