用全景高斯点阵生成逼真车载激光雷达点云，速度快效果好。
GS-LiDAR: Generating Realistic LiDAR Point Clouds with Panoramic Gaussian Splatting
- 用带周期振动的2D高斯点代替神经辐射场，提升几何重建精度
- 在KITTI-360和nuScenes上实现更高视觉质量与更快渲染速度
- 适合自动驾驶仿真系统，尤其擅长动态场景点云生成
LiDAR新视角合成（NVS）作为激光雷达模拟中的新兴任务，可从新视角生成有价值的模拟点云数据，助力自动驾驶系统。然而现有方法多依赖神经辐射场（NeRF）作为3D表示，训练与渲染计算开销大，且因设计用于对称场景，难以适配驾驶环境。为此，我们提出GS-LiDAR，一种基于全景高斯点阵生成逼真激光雷达点云的新框架。该方法采用具有周期振动特性的2D高斯基元，实现对驾驶场景中静态与动态元素的精准几何重建。我们进一步引入基于全景激光雷达监督的显式射线-点阵相交全景渲染技术，并将强度及射线缺失的球谐（SH）系数嵌入高斯基元，提升渲染点云的真实感。在KITTI-360与nuScenes数据集上的大量实验表明，本方法在量化指标、视觉质量以及训练与渲染效率方面均具显著优势。
原文摘要 · Abstract (English)
LiDAR novel view synthesis (NVS) has emerged as a novel task within LiDAR simulation, offering valuable simulated point cloud data from novel viewpoints to aid in autonomous driving systems. However, existing LiDAR NVS methods typically rely on neural radiance fields (NeRF) as their 3D representation, which incurs significant computational costs in both training and rendering. Moreover, NeRF and its variants are designed for symmetrical scenes, making them ill-suited for driving scenarios. To address these challenges, we propose GS-LiDAR, a novel framework for generating realistic LiDAR point clouds with panoramic Gaussian splatting. Our approach employs 2D Gaussian primitives with periodic vibration properties, allowing for precise geometric reconstruction of both static and dynamic elements in driving scenarios. We further introduce a novel panoramic rendering technique with explicit ray-splat intersection, guided by panoramic LiDAR supervision. By incorporating intensity and ray-drop spherical harmonic (SH) coefficients into the Gaussian primitives, we enhance the realism of the rendered point clouds. Extensive experiments on KITTI-360 and nuScenes demonstrate the superiority of our method in terms of quantitative metrics, visual quality, as well as training and rendering efficiency.
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