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 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]