Core Insights - The article highlights the advancements in 3D Gaussian Splatting (3DGS) technology, particularly its application in autonomous driving, and emphasizes the need for structured learning pathways in this rapidly evolving field [2][4]. Group 1: 3DGS Technology and Developments - Tesla's introduction of 3D Gaussian Splatting at ICCV has garnered significant attention, indicating a shift towards feed-forward GS algorithms in the industry [2]. - The rapid iteration of 3DGS technology includes static reconstruction (3DGS), dynamic reconstruction (4DGS), and surface reconstruction (2DGS), showcasing the need for effective learning resources [4]. Group 2: Course Offering - A comprehensive course titled "3DGS Theory and Algorithm Practical Tutorial" has been developed to provide a structured learning roadmap for newcomers, covering essential theories and practical applications [4]. - The course is designed to help participants understand point cloud processing, deep learning, real-time rendering, and coding practices, with a focus on hands-on experience [4]. Group 3: Course Structure - The course consists of six chapters, starting with foundational knowledge in computer graphics and progressing to advanced topics such as feed-forward 3DGS and its applications in autonomous driving [8][9][10][11][12]. - Each chapter includes practical assignments and discussions to enhance understanding and application of the concepts learned [8][9][10][11][12]. Group 4: Target Audience and Prerequisites - The course is aimed at individuals with a background in computer graphics, visual reconstruction, and programming, particularly those interested in pursuing careers in the 3DGS field [17]. - Participants are expected to have a foundational understanding of probability, linear algebra, and programming languages such as Python and PyTorch [17].