RoGs: Large Scale Road Surface Reconstruction with Meshgrid Gaussian

TL;DR

The paper introduces RoGs, a novel large-scale road surface reconstruction method using meshgrid Gaussian surfels, which improves speed and quality over previous mesh-based approaches for autonomous driving applications.

Contribution

It proposes a meshgrid Gaussian surfel model with pose-based initialization, enabling faster and more accurate large-scale road surface reconstruction.

Findings

Achieves significant speedups in reconstruction process.

Improves reconstruction quality compared to previous methods.

Performs well in challenging real-world scenes.

Abstract

Road surface reconstruction plays a crucial role in autonomous driving, which can be used for road lane perception and autolabeling. Recently, mesh-based road surface reconstruction algorithms have shown promising reconstruction results. However, these mesh-based methods suffer from slow speed and poor reconstruction quality. To address these limitations, we propose a novel large-scale road surface reconstruction approach with meshgrid Gaussian, named RoGs. Specifically, we model the road surface by placing Gaussian surfels in the vertices of a uniformly distributed square mesh, where each surfel stores color, semantic, and geometric information. This square mesh-based layout covers the entire road with fewer Gaussian surfels and reduces the overlap between Gaussian surfels during training. In addition, because the road surface has no thickness, 2D Gaussian surfel is more consistent with the physical reality of the road surface than 3D Gaussian sphere. Then, unlike previous initialization methods that rely on point clouds, we introduce a vehicle pose-based initialization method to initialize the height and rotation of the Gaussian surfel. Thanks to this meshgrid Gaussian modeling and pose-based initialization, our method achieves significant speedups while improving reconstruction quality. We obtain excellent results in reconstruction of road surfaces in a variety of challenging real-world scenes.