Large-scale road surface reconstruction is becoming important to autonomous driving systems, as it provides valuable training and testing data effectively. In this paper, we introduce a simple yet efficient method, RoMe, for large-scale Road surface reconstruction via Mesh representations. To simplify the problem, RoMe decomposes a 3D road surface into a triangle-mesh and a multilayer perception network to model the road elevation implicitly. To retain fine surface details, each mesh vertex has two extra attributes, namely color and semantics. To improve the efficiency of RoMe in large-scale environments, a novel waypoint sampling method is introduced. As such, RoMe can properly preserve road surface details, with only linear computational complexity to road areas. In addition, to improve the accuracy of RoMe, extrinsics optimization is proposed to mitigate inaccurate extrinsic calibrations. Experimental results on popular public datasets also demonstrate the high efficiency and accuracy of RoMe.

本研究利用网格表示法提出了高效的方法——RoMe，通过多层感知机对三维道路进行了分解，并在每个网格顶点上添加了颜色和语义属性，以保留精细的路面细节。同时引入了一种新的路点抽样方法以提高效率，并提出外部校准优化以提高准确性。在公共数据集上的实验结果表明了RoMe的高效性和准确性。

RoMe：通过网格表达实现大规模道路表面重建