Abstract
Road surface reconstruction is essential for autonomous driving, supporting centimeter-accurate lane perception and high-definition mapping in complex urban this http URL recent methods based on mesh rendering or 3D Gaussian splatting (3DGS) achieve promising results under clean and static conditions, they remain vulnerable to occlusions from dynamic agents, visual clutter from static obstacles, and appearance degradation caused by lighting and weather changes. We present a robust reconstruction framework that integrates occlusion-aware 2D Gaussian surfels with semantic-guided color enhancement to recover clean, consistent road surfaces. Our method leverages a planar-adapted Gaussian representation for efficient large-scale modeling, employs segmentation-guided video inpainting to remove both dynamic and static foreground objects, and enhances color coherence via semantic-aware correction in HSV space. Extensive experiments on urban-scale datasets demonstrate that our framework produces visually coherent and geometrically faithful reconstructions, significantly outperforming prior methods under real-world conditions.
Abstract (translated)
路面重建对于自动驾驶至关重要，它支持在复杂的城市环境中实现厘米级精度的车道感知和高清晰度地图绘制。尽管最近基于网格渲染或三维高斯喷射（3DGS）的方法在干净且静态条件下取得了有希望的结果，但它们仍然容易受到动态对象遮挡、静态障碍物造成的视觉混乱以及光照和天气变化引起的外观退化的影响。我们提出了一种稳健的重建框架，该框架结合了感知遮挡的二维高斯表面元素与语义引导的颜色增强，以恢复干净且一致的道路表面。 我们的方法利用了一种平面适应型的高斯表示法进行高效的大规模建模，并采用了分割引导视频修复技术来移除动态和静态前景对象。同时，通过在HSV空间中的语义感知校正来增强颜色的一致性。 在城市规模的数据集上进行的大量实验表明，我们的框架能够生成视觉连贯且几何准确的重建结果，在实际条件下显著优于先前的方法。
URL
https://arxiv.org/abs/2507.23340
