Core Viewpoint - The article discusses the advancements in 3D Gaussian Splatting (3DGS) technology in the autonomous driving sector, highlighting the introduction of feed-forward GS algorithms and the need for effective learning pathways for newcomers in the field [2][4]. Group 1: Course Overview - A new course titled "3DGS Theory and Algorithm Practical Tutorial" has been developed to provide a comprehensive learning roadmap for 3DGS technology, covering both theoretical and practical aspects [4]. - The course is designed to help participants understand point cloud processing, deep learning theories, real-time rendering, and coding practices [4]. Group 2: Course Structure - The course consists of six chapters, starting with foundational knowledge in computer graphics and moving through the principles and algorithms of 3DGS, including dynamic and surface reconstruction [8][9]. - The third chapter focuses on the application of 3DGS in autonomous driving simulation, providing insights into key works and tools used in the industry [10]. - Subsequent chapters explore important research directions in 3DGS, including COLMAP extensions and depth estimation, as well as the emerging feed-forward 3DGS techniques [11][12]. Group 3: Target Audience and Requirements - The course is aimed at individuals with a background in computer graphics, visual reconstruction, and programming, specifically those familiar with Python and PyTorch [17]. - Participants are expected to have access to a GPU with a recommended capability of 4090 or higher to effectively engage with the course content [17].

Core Insights - The article emphasizes the rapid technological iteration in 3DGS (3D Graphics Systems), highlighting the transition from static reconstruction (3DGS) to dynamic reconstruction (4DGS) and surface reconstruction (2DGS) [1] - A new course titled "3DGS Theory and Algorithm Practical Tutorial" has been developed to provide a structured learning roadmap for individuals interested in entering the field, covering essential theories and practical coding skills [1] Course Overview - The course is designed to help newcomers understand the foundational concepts of computer graphics, including implicit and explicit representations of 3D space, rendering pipelines, ray tracing, and radiation field rendering [5] - It introduces commonly used development tools such as SuperSplat and COLMAP, along with the mainstream algorithm framework Gsplat [5] Chapter Summaries - **Chapter 1: Background Knowledge** This chapter provides an overview of 3DGS, starting with basic computer graphics concepts and tools necessary for model training [5] - **Chapter 2: Principles and Algorithms** Focuses on the core principles and pseudocode of 3DGS, covering dynamic reconstruction, surface reconstruction, and ray tracing, utilizing the NVIDIA open-source 3DGRUT framework for practical learning [6] - **Chapter 3: Autonomous Driving 3DGS** Concentrates on key works in the field, such as Street Gaussian and OmniRe, and uses DriveStudio for practical applications [7] - **Chapter 4: Important Research Directions** Discusses significant research areas in 3DGS, including COLMAP extensions and depth estimation, and their relevance to both industry and academia [8] - **Chapter 5: Feed-Forward 3DGS** Explores the rise of feed-forward 3DGS, detailing its development and algorithmic principles, along with recent works like AnySplat and WorldSplat [9] - **Chapter 6: Q&A Discussion** Organizes online discussions for participants to address industry needs, pain points, and open questions, facilitating deeper engagement with instructors [10] Target Audience and Learning Outcomes - The course is aimed at individuals with a foundational understanding of computer graphics, visual reconstruction, and programming in Python and PyTorch, who are looking to enhance their knowledge and skills in 3DGS [14] - Participants will gain comprehensive theoretical knowledge and practical experience in 3DGS algorithm development and frameworks, preparing them for various career opportunities in the field [14]