BézierGS：基于贝塞尔曲线高斯泼溅的动态城市场景重建
BézierGS: Dynamic Urban Scene Reconstruction with Bézier Curve Gaussian Splatting
AI总结：
针对自动驾驶城市场景重建依赖物体姿态标注的问题，提出贝塞尔曲线高斯泼溅方法，用可学习曲线建模动态物体轨迹并自动纠正姿态误差，在Waymo和nuPlan数据集上优于现有方法。
摘要：
街道场景的真实感重建对于开发自动驾驶中的真实世界模拟器至关重要。现有的大多数方法依赖于物体姿态标注，利用这些姿态来重建动态物体并在渲染过程中移动它们。这种对高精度物体标注的依赖限制了大规模和广泛的场景重建。为了解决这一挑战，我们提出了贝塞尔曲线高斯泼溅（BézierGS），该方法使用可学习的贝塞尔曲线来表示动态物体的运动轨迹。该方法充分利用了动态物体的时间信息，并通过可学习的曲线建模自动纠正姿态误差。通过引入对动态物体渲染的额外监督和曲线间一致性约束，我们实现了对场景元素的合理且准确的分离与重建。在Waymo开放数据集和nuPlan基准上的大量实验表明，BézierGS在动态和静态场景组件的重建以及新视角合成方面均优于最先进的替代方法。
英文摘要：
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.
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。
