ICCV 2025自动驾驶场景重建工作汇总！这个方向大有可为~

Core Viewpoint - The article emphasizes the advancements in autonomous driving scene reconstruction, highlighting the integration of various technologies and the collaboration among top universities and research institutions in this field [2][12]. Summary by Sections Section 1: Overview of Autonomous Driving Scene Reconstruction - The article discusses the importance of dynamic and static scene reconstruction in autonomous driving, focusing on the need for precise color and geometric information through the integration of lidar and visual data [2]. Section 2: Research Contributions - Several notable research works from prestigious institutions such as Tsinghua University, Nankai University, Fudan University, and the University of Illinois Urbana-Champaign are mentioned, showcasing their contributions to the field [5][6][10][11]. Section 3: Educational Initiatives - The article promotes a comprehensive course on 3D Gaussian Splatting (3DGS), designed in collaboration with leading experts, aimed at providing in-depth knowledge and practical skills in autonomous driving scene reconstruction [15][19]. Section 4: Course Structure - The course is structured into eight chapters, covering foundational algorithms, technical details of 3DGS, static and dynamic scene reconstruction, surface reconstruction, and practical applications in autonomous driving [19][21][23][25][27][29][31][33]. Section 5: Target Audience - The course is targeted at researchers, students, and professionals interested in 3D reconstruction, requiring a foundational understanding of 3DGS and related technologies [36][37].