用多趟驾驶数据重建高精度道路场景，支持任意视角生成。
MTGS: Multi-Traversal Gaussian Splatting
- 构建共享静态结构+各趟动态节点的图模型，分离处理动态物体与外观变化。
- 在nuPlan数据集上，新视角合成质量提升23.5%（LPIPS），几何精度提高46.3%。
- 适合自动驾驶仿真、高精地图重建等需要多视角一致性的场景。
日常通勤或自动驾驶车队采集的多趟数据为道路区域提供了多个视角，具有重建高质量新视角图像的巨大潜力，对自动驾驶模拟器至关重要。然而，多趟数据中固有的外观差异和动态物体导致重建效果不佳。为此，我们提出多趟高斯点云方法（MTGS），通过建模共享静态几何结构，分别处理动态元素和外观变化，实现高质量驾驶场景重建。该方法采用包含共享静态节点和每趟独立动态节点的多趟动态场景图，并引入可学习球谐系数残差的颜色校正节点。实验基于大规模驾驶数据集nuPlan进行，结果表明，相较于单趟基线方法，MTGS在LPIPS指标上提升23.5%，几何精度提升46.3%。代码与数据将公开。
原文摘要 · Abstract (English)
Multi-traversal data, commonly collected through daily commutes or by self-driving fleets, provides multiple viewpoints for scene reconstruction within a road block. This data offers significant potential for high-quality novel view synthesis, which is crucial for applications such as autonomous vehicle simulators. However, inherent challenges in multi-traversal data often result in suboptimal reconstruction quality, including variations in appearance and the presence of dynamic objects. To address these issues, we propose Multi-Traversal Gaussian Splatting (MTGS), a novel approach that reconstructs high-quality driving scenes from arbitrarily collected multi-traversal data by modeling a shared static geometry while separately handling dynamic elements and appearance variations. Our method employs a multi-traversal dynamic scene graph with a shared static node and traversal-specific dynamic nodes, complemented by color correction nodes with learnable spherical harmonics coefficient residuals. This approach enables high-fidelity novel view synthesis and provides flexibility to navigate any viewpoint. We conduct extensive experiments on a large-scale driving dataset, nuPlan, with multi-traversal data. Our results demonstrate that MTGS improves LPIPS by 23.5% and geometry accuracy by 46.3% compared to single-traversal baselines. The code and data would be available to the public.
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