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].

一般对应闭环仿真或场景重建的算法岗位，日常工作聚焦在： 一般公司需要 5-20 人的算法团队支撑闭环仿真中重建的优化。除此之外，在和其他小伙伴的交流过程中，我们发现 云端的数据生产 也有需求，比如BEV视角下 的静态路面重建，即2DGS重建，可以应用到静态真值生产中，比如RoGS。最近小米的ParkGaussian把GS应用到 泊车场景中 。综合来说，每个方向都需要至少10 人左右的算法团队规模支撑最基本的功能需求。 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 上周有企业做招聘的朋友和柱哥聊了聊，26年需要在重建方向投入一些HC，借这个机会，和大家盘一下GS在自驾业内的职位和需求。 对于重建来说，主要是用于测试的闭环仿真。闭环仿真的用途比较明确，对于一个离线的clip数据，用3DGS重建动静态元素，验证新模型在重建clip上的效果，是 否可以预测合理的新轨迹，沿用新的轨迹能否正常行驶。 添加助理领取优惠！ 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 ...

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 >>自动驾驶前沿信息获取 → 自动驾驶之心知识星球 论文作者 | Xiaobao Wei等 编辑 | 自动驾驶之心 高斯泼溅的风，刮到了自驾的每个角落。 一大早看到了小米&杭电在泊车场景重建中的工作ParkGaussian。 相比英伟达3DGUT和OmniRe提升挺大，分享给大家。 泊车是自动驾驶系统（ADS）的关键任务，在车位拥挤且无GPS信号的环境中面临独特挑战。现有研究主要集中于二维车位感知、建图与定位，而三维重建领域的探 索仍显不足——该技术对于捕捉泊车场景中的复杂空间几何结构至关重要。单纯提升重建泊车场景的视觉质量并不能直接助力自动泊车，因为泊车系统的核心入口是 车位感知模块。 为解决这些局限，小米汽车联合杭州电子科技大学构建了首个专为泊车场景重建设计的基准数据集ParkRecon3D，其包含来自四台已完成外参标定的环视鱼眼相机的 传感器数据，以及密集的车位标注信息。在此基础上，本文提出了ParkGaussian框架，这是首个将3D高斯Splatting（3DGS）融入泊车场景重建的方案。为进一步提 ...