用语义引导的补全技术，修复动态遮挡下的道路表面重建。
MagicRoad: Semantic-Aware 3D Road Surface Reconstruction via Obstacle Inpainting
- 用2D高斯面片+语义分割，智能移除遮挡物并补全道路
- 在真实城市数据集上实现厘米级精度，优于现有方法
- 适合自动驾驶高精地图构建与复杂路况重建
道路表面重建对自动驾驶至关重要，支持复杂城市环境中厘米级车道感知与高精地图构建。尽管基于网格渲染或3D高斯泼溅（3DGS）的方法在干净静态条件下表现良好，但仍易受动态物体遮挡、静态障碍物视觉干扰及光照天气变化导致的外观退化影响。本文提出一种鲁棒重建框架，融合遮挡感知的2D高斯面片与语义引导的颜色增强，以恢复清晰一致的道路表面。方法采用平面自适应的高斯表示实现大规模高效建模，通过分割引导的视频修复去除动态与静态前景物体，并在HSV空间中利用语义感知校正提升颜色一致性。在城市尺度数据集上的大量实验表明，该框架在真实场景下生成视觉连贯且几何忠实的重建结果，显著优于现有方法。
原文摘要 · Abstract (English)
Road surface reconstruction is essential for autonomous driving, supporting centimeter-accurate lane perception and high-definition mapping in complex urban environments.While 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.
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