用贝塞尔曲线自动建模动态物体运动轨迹，提升街景重建精度。
BézierGS: Dynamic Urban Scene Reconstruction with Bézier Curve Gaussian Splatting
- 用可学习的贝塞尔曲线表示动态物体运动轨迹，无需高精度标注。
- 在Waymo和nuPlan数据集上，动态与静态场景重建均优于现有方法。
- 适合自动驾驶仿真、城市三维重建等需要高精度动态场景的领域。
真实街景重建对自动驾驶仿真系统至关重要。现有方法依赖高精度物体位姿标注，通过标注位姿进行动态物体重建与渲染，但该依赖限制了大规模场景重建。为此，我们提出贝塞尔曲线高斯点云（BézierGS），利用可学习的贝塞尔曲线表示动态物体运动轨迹，充分挖掘动态物体的时间信息，并通过可学习曲线建模自动修正位姿误差。引入动态物体渲染额外监督与曲线间一致性约束，实现场景元素的合理且准确分离与重建。在Waymo Open Dataset和nuPlan基准上的大量实验表明，BézierGS在动态与静态场景重建及新视角合成方面均优于现有先进方法。
原文摘要 · Abstract (English)
The realistic reconstruction of street scenes is critical for developing real-world simulators in autonomous driving. Most existing methods rely on object pose annotations, using these poses to reconstruct dynamic objects and move them during the rendering process. This dependence on high-precision object annotations limits large-scale and extensive scene reconstruction. To address this challenge, we propose Bézier curve Gaussian splatting (BézierGS), which represents the motion trajectories of dynamic objects using learnable Bézier curves. This approach fully leverages the temporal information of dynamic objects and, through learnable curve modeling, automatically corrects pose errors. By introducing additional supervision on dynamic object rendering and inter-curve consistency constraints, we achieve reasonable and accurate separation and reconstruction of scene elements. Extensive experiments on the Waymo Open Dataset and the nuPlan benchmark demonstrate that BézierGS outperforms state-of-the-art alternatives in both dynamic and static scene components reconstruction and novel view synthesis.
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