点击下方 卡片 ，关注" 自动驾驶之心 "公众号 戳我-> 领取 自动驾驶近30个 方向 学习 路线 特斯拉ICCV的分享吸引了很多小伙伴的关注，里面的3D Gaussian的引入可谓是一大亮点。基本上可以判断特斯拉是基于前馈式GS算法实现的，近期学术界的工 作也相当多，像小米的WorldSplat和清华最新的DGGT等等。3DGS正在自动驾驶焕发又一轮生机。目前业内普遍的共识是引入了前馈GS重建场景在利用生成技术 生成新视角，不少公司都在开放HC招聘。 但3DGS的技术迭代速度远超想象，静态重建3DGS、动态重建4DGS、表面重建2DGS，再到feed-forward 3DGS。很多同学想入门却苦于没有有效的学习路线图： 既要吃透点云处理、深度学习等理论，又要掌握实时渲染、代码实战。 为此自动驾驶之心联合 工业界算法专家 开展了这门 《3DGS理论与算法实战教程》！ 我 们花了两个月的时间设计了 一套3DGS的学习路线图，从原理到实战细致展开。全面吃透3DGS技术栈。 正式开课！添加助理咨询课程 讲师介绍 Chris：QS20 硕士，现任某Tier1厂算法专家，目前从事端到端仿真、多模态大模型、世界模型等前 ...

Core Viewpoint - The article emphasizes the rapid advancements in 3D Gaussian Splatting (3DGS) technology within the autonomous driving sector, highlighting the need for structured learning pathways for newcomers in the field [2][4]. Group 1: Technology Highlights - Tesla's introduction of 3D Gaussian Splatting at ICCV has garnered significant attention, indicating a shift towards feed-forward GS algorithms for scene reconstruction [2]. - The iterative development of 3DGS technology includes static 3D reconstruction, dynamic 4D reconstruction, and surface reconstruction, showcasing its evolving nature [4]. Group 2: Course Offering - A comprehensive course titled "3DGS Theory and Algorithm Practical Tutorial" has been designed to provide a structured learning roadmap for 3DGS, covering both theoretical foundations and practical applications [4]. - The course will be taught by an expert with extensive experience in 3D reconstruction and algorithm development, ensuring high-quality instruction [5]. Group 3: Course Structure - The course consists of six chapters, starting with foundational knowledge in computer graphics and progressing through principles, algorithms, and specific applications in autonomous driving [8][9][10][11][12]. - Each chapter is designed to build upon the previous one, culminating in discussions about current industry needs and research directions in 3DGS [11][12][13]. 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 autonomous driving industry [17]. - Participants are expected to have a foundational understanding of relevant mathematical concepts and programming languages, which will facilitate their learning experience [17].