肝了几个月，新的端到端闭环仿真系统终于用上了。

Core Viewpoint - The article discusses the development and implementation of the Street Gaussians algorithm for dynamic scene representation in autonomous driving, highlighting its efficiency in training and rendering compared to previous methods [2][3]. Group 1: Background and Challenges - Previous methods faced challenges such as slow training and rendering speeds, as well as inaccuracies in vehicle pose tracking [3]. - Street Gaussians aims to generate realistic images for view synthesis in dynamic urban street scenes by modeling them as a combination of foreground moving vehicles and static backgrounds [3][4]. Group 2: Technical Implementation - The background model is represented as a set of points in world coordinates, each assigned a 3D Gaussian to represent geometry and color, with parameters optimized to avoid invalid values [8]. - The object model for moving vehicles includes a set of optimizable tracking poses and point clouds, with similar Gaussian attributes to the background model but defined in local coordinates [11]. - A 4D spherical harmonic model is introduced to encode temporal information into the appearance of moving vehicles without high storage costs [12]. Group 3: Initialization and Data Handling - Street Gaussians utilizes aggregated LiDAR point clouds for initialization, addressing the limitations of traditional SfM point clouds in urban environments [17]. - For objects with fewer than 2,000 LiDAR points, random sampling is employed to ensure sufficient data for model initialization [17]. Group 4: Course and Learning Opportunities - The article promotes a specialized course on 3D Gaussian Splatting (3DGS), covering various subfields and practical applications in autonomous driving, aimed at enhancing understanding and implementation skills [26][35].