Core Viewpoint - The article discusses the growing demand for algorithm teams in the field of 3DGS (3D Gaussian Splatting) for autonomous driving, highlighting the need for skilled professionals and the development of a comprehensive training course to address this gap [2][3]. Group 1: Industry Demand and Job Roles - Companies are looking to invest in headcount (HC) for testing and closed-loop simulation in the autonomous driving sector, indicating a clear need for algorithm teams ranging from 5 to 20 members to support optimization in closed-loop simulations [2][3]. - The demand for cloud data production is also noted, particularly for static road surface reconstruction, which requires a minimum team size of around 10 people to meet basic functional needs [3]. Group 2: 3DGS Development and Learning Path - The article outlines a structured learning path for 3DGS, starting from static reconstruction to dynamic reconstruction and surface reconstruction, culminating in mixed scene reconstruction and feed-forward GS [3]. - A course titled "3DGS Theory and Algorithm Practical Tutorial" has been developed to provide a detailed roadmap for understanding 3DGS technology, covering principles and practical applications [3]. Group 3: Course Structure and Content - The course consists of six chapters, covering topics such as background knowledge, principles and algorithms of 3DGS, technical explanations for autonomous driving, important research directions, and feed-forward 3DGS [6][8][9][10][11][12]. - Each chapter is designed to build upon the previous one, ensuring a comprehensive understanding of 3DGS and its applications in the industry [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 related technologies, as well as those familiar with Python and PyTorch [17]. - Participants are expected to have a foundational understanding of probability theory and linear algebra, which are essential for mastering the 3DGS technology stack [17].

Core Viewpoint - The article discusses the latest work from Fudan University on a method called BezierGS, which utilizes Bezier curves for dynamic urban scene reconstruction, crucial for developing closed-loop simulations in autonomous driving [4][5]. Group 1: Methodology and Contributions - BezierGS addresses the limitations of existing methods that rely on precise pose annotations for dynamic targets, which restricts large-scale scene reconstruction [4][7]. - The method employs learnable Bezier curves to represent the motion trajectories of dynamic targets, effectively utilizing temporal information and calibrating pose errors [4][8]. - Extensive experiments on the Waymo open dataset and nuPlan benchmark demonstrate that BezierGS outperforms state-of-the-art alternatives in both dynamic and static scene target reconstruction and novel view synthesis [4][14]. Group 2: Advantages and Future Directions - The approach aims to build a high-quality street scene for training autonomous models, reducing data collection costs and reliance on bounding box accuracy, which is often imprecise in current datasets [6]. - Future exploration will focus on creating a true autonomous driving world model, although the current work is limited to trajectory interpolation and cannot extrapolate beyond the trajectory [6]. - The introduction of additional supervision for dynamic target rendering enhances the separation and reconstruction of scene elements, leading to more accurate simulations [8][49]. Group 3: Experimental Results - The experiments conducted on the Waymo and nuPlan datasets show significant improvements in reconstruction quality, with BezierGS achieving higher PSNR and SSIM scores compared to existing methods [36][41]. - Specifically, in the Waymo dataset, BezierGS achieved a PSNR of 33.98 and an SSIM of 0.934, outperforming other methods by notable margins [36][37]. - In the nuPlan benchmark, BezierGS demonstrated a PSNR improvement of 3.04 dB and a reduction in LPIPS by 16.35%, showcasing its effectiveness in handling complex dynamic scenes [41].