聊聊自动驾驶闭环仿真和3DGS！

Core Viewpoint - The article discusses the development and implementation of the Street Gaussians algorithm, which aims to efficiently model dynamic street scenes for autonomous driving simulations, addressing previous limitations in training and rendering speeds [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]. - The Street Gaussians algorithm represents dynamic urban street scenes as a combination of point-based backgrounds and foreground objects, utilizing optimized vehicle tracking poses [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 depict geometric shape and color, with parameters including covariance matrices and position vectors [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]. Group 3: Innovations in Appearance Modeling - The article introduces a 4D spherical harmonic model to encode temporal information into the appearance of moving vehicles, reducing storage costs compared to traditional methods [12]. - The effectiveness of the 4D spherical harmonic model is demonstrated, showing significant improvements in rendering results and reducing artifacts [16]. Group 4: Initialization Techniques - Street Gaussians utilizes aggregated LiDAR point clouds for initialization, addressing the limitations of traditional SfM point clouds in urban environments [17]. Group 5: 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][30].