Accurate and realistic 3D scene reconstruction enables the lifelike creation of autonomous driving simulation environments. With advancements in 3D Gaussian Splatting (3DGS), previous studies have applied it to reconstruct complex dynamic driving scenes. These methods typically require expensive LiDAR sensors and pre-annotated datasets of dynamic objects. To address these challenges, we propose OG-Gaussian, a novel approach that replaces LiDAR point clouds with Occupancy Grids (OGs) generated from surround-view camera images using Occupancy Prediction Network (ONet). Our method leverages the semantic information in OGs to separate dynamic vehicles from static street background, converting these grids into two distinct sets of initial point clouds for reconstructing both static and dynamic objects. Additionally, we estimate the trajectories and poses of dynamic objects through a learning-based approach, eliminating the need for complex manual annotations. Experiments on Waymo Open dataset demonstrate that OG-Gaussian is on par with the current state-of-the-art in terms of reconstruction quality and rendering speed, achieving an average PSNR of 35.13 and a rendering speed of 143 FPS, while significantly reducing computational costs and economic overhead.

本研究针对自主驾驶仿真环境中3D场景重建的准确性和现实性问题，提出了一种新型的OG-Gaussian方法，利用从环视相机图像生成的占用网格（OGs）来替代昂贵的激光雷达点云。这一方法通过语义信息将动态车辆与静态街道背景分离，并通过学习方法估计动态对象的轨迹和姿态，降低了手动标注的复杂性，实验结果显示该方法在重建质量和渲染速度上达到现有技术的水平。 

基于占用的街道高斯模型OG-Gaussian在自主驾驶中的应用