RoMe：面向基于网格表示的大规模路面重建
RoMe: Towards Large Scale Road Surface Reconstruction via Mesh Representation
AI总结：
针对自动驾驶中大规模路面重建的效率与精度需求，提出基于网格表示的RoMe框架，结合航点采样与外参优化，在速度、精度和鲁棒性上表现优异，还可支撑自动标注任务。
摘要：
在自动驾驶应用中，准确高效的路面重建至关重要。本文提出RoMe，一种专为大规模路面鲁棒重建设计的新型框架。RoMe利用独特的网格表示，确保重建的路面准确且与语义无缝对齐。为解决计算效率方面的挑战，我们提出了一种航点采样策略，使RoMe能够通过聚焦子区域并随后合并它们来重建广阔环境。此外，我们引入了一个外参优化模块，以增强对外参标定不准确情况的鲁棒性。我们在公开数据集和野外数据上开展的大量评估，凸显了RoMe在速度、准确性和鲁棒性方面的优势。例如，从数千张图像中重建600*600平方米的路面仅需2个GPU小时。值得注意的是，RoMe的能力不止于单纯的重建，还能为自动驾驶应用中的自动标注任务提供重要价值。所有相关数据和代码可在https://github.com/DRosemei/RoMe获取。
英文摘要：
In autonomous driving applications, accurate and efficient road surface reconstruction is paramount. This paper introduces RoMe, a novel framework designed for the robust reconstruction of large-scale road surfaces. Leveraging a unique mesh representation, RoMe ensures that the reconstructed road surfaces are accurate and seamlessly aligned with semantics. To address challenges in computational efficiency, we propose a waypoint sampling strategy, enabling RoMe to reconstruct vast environments by focusing on sub-areas and subsequently merging them. Furthermore, we incorporate an extrinsic optimization module to enhance the robustness against inaccuracies in extrinsic calibration. Our extensive evaluations of both public datasets and wild data underscore RoMe's superiority in terms of speed, accuracy, and robustness. For instance, it costs only 2 GPU hours to recover a road surface of 600*600 square meters from thousands of images. Notably, RoMe's capability extends beyond mere reconstruction, offering significant value for autolabeling tasks in autonomous driving applications. All related data and code are available at https://github.com/DRosemei/RoMe.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
