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.

翻译：大规模路面重建对自动驾驶系统日益重要，因为它能有效提供宝贵的训练和测试数据。本文提出一种简洁高效的方法RoMe，通过网格表示实现大规模路面重建。为简化问题，RoMe将三维路面分解为三角形网格和多层感知网络，以此隐式建模路面高程。为保留精细表面细节，每个网格顶点附加两个额外属性，即颜色和语义。为提升RoMe在大规模环境中的效率，引入一种新颖的航点采样方法。这样，RoMe能以仅与路面面积成线性关系的计算复杂度，恰当保留路面细节。此外，为提升RoMe精度，提出外参优化方法以缓解不准确的外参标定问题。在公开数据集上的实验结果也证明了RoMe的高效性和准确性。