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的分享指明了智驾下一阶段发展的方向 - 端到端+生成式GS，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。具体移步： Tesla终于分享点东西了，世界模型和闭环评测都强的可怕...... 为什么前馈GS会引起国内重视，柱哥认为主要有几点： 但这个领域太新了，几乎没有什么有效的学习资料，对于很多初学者来说是非常困难的。我们反过来梳理下3DGS的发展路线，会找到一条比较明确的路线： 静态 重建3DGS → 动态重建4DGS → 表面重建2DGS → 场景重建混合GS → 前馈GS。 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。 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、世界模型等前沿算法的预研和量产，参与过全球TOP主机厂仿真引擎以及工具 链开发，拥 ...

Core Insights - The article highlights the introduction of 3D Gaussian (3DGS) technology by Tesla, indicating a significant advancement in autonomous driving through the use of feed-forward GS algorithms [1][3] - There is a consensus in the industry regarding the rapid iteration of 3DGS technology, with various companies actively hiring for related positions [1][3] Group 1: Course Overview - A new course titled "3DGS Theory and Algorithm Practical Tutorial" has been developed to provide a structured learning path for newcomers to the 3DGS field, covering both theoretical and practical aspects [3][7] - The course is designed to help participants understand point cloud processing, deep learning, real-time rendering, and coding practices [3][7] Group 2: Course Structure - The course consists of six chapters, starting with foundational knowledge in computer graphics and progressing to advanced topics such as dynamic reconstruction and surface reconstruction [7][8] - Each chapter includes practical assignments and discussions on relevant algorithms and frameworks, such as the use of NVIDIA's open-source 3DGRUT framework [8][9] 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 [16] - Participants are expected to have a GPU with a recommended capability of 4090 or higher to effectively engage with the course content [16] Group 4: Learning Outcomes - By the end of the course, participants will have a comprehensive understanding of the 3DGS technology stack, including algorithm development and the ability to train open-source models [16] - The course also facilitates networking opportunities with peers from academia and industry, enhancing career prospects in the field [16]