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。

RoGS：基于 2D 高斯散布的大规模路面重建

RoGS: Large Scale Road Surface Reconstruction based on 2D Gaussian  Splatting

Road surface reconstruction plays a vital role in autonomous driving systems,
enabling road lane perception and high-precision mapping. Recently, neural
implicit encoding has achieved remarkable results in scene representation,
particularly in the realistic rendering of scene textures. However, it faces
challenges in directly representing geometric information for large-scale
scenes. To address this, we propose EMIE-MAP, a novel method for large-scale
road surface reconstruction based on explicit mesh and implicit encoding. The
road geometry is represented using explicit mesh, where each vertex stores
implicit encoding representing the color and semantic information. To overcome
the difficulty in optimizing road elevation, we introduce a trajectory-based
elevation initialization and an elevation residual learning method based on
Multi-Layer Perceptron (MLP). Additionally, by employing implicit encoding and
multi-camera color MLPs decoding, we achieve separate modeling of scene
physical properties and camera characteristics, allowing surround-view
reconstruction compatible with different camera models. Our method achieves
remarkable road surface reconstruction performance in a variety of real-world
challenging scenarios.

基于显式网格与隐式编码，提出一种用于大规模道路表面重建的新方法，通过区分场景物理属性和相机特性实现视角适应的道路重建，取得了优异的重建性能。