Core Viewpoint - The article emphasizes the advancements in autonomous driving scene reconstruction, highlighting the integration of various technologies and the collaboration among top universities and research institutions in this field [2][12]. Summary by Sections Section 1: Overview of Autonomous Driving Scene Reconstruction - The article discusses the importance of dynamic and static scene reconstruction in autonomous driving, focusing on the need for precise color and geometric information through the integration of lidar and visual data [2]. Section 2: Research Contributions - Several notable research works from prestigious institutions such as Tsinghua University, Nankai University, Fudan University, and the University of Illinois Urbana-Champaign are mentioned, showcasing their contributions to the field [5][6][10][11]. Section 3: Educational Initiatives - The article promotes a comprehensive course on 3D Gaussian Splatting (3DGS), designed in collaboration with leading experts, aimed at providing in-depth knowledge and practical skills in autonomous driving scene reconstruction [15][19]. Section 4: Course Structure - The course is structured into eight chapters, covering foundational algorithms, technical details of 3DGS, static and dynamic scene reconstruction, surface reconstruction, and practical applications in autonomous driving [19][21][23][25][27][29][31][33]. Section 5: Target Audience - The course is targeted at researchers, students, and professionals interested in 3D reconstruction, requiring a foundational understanding of 3DGS and related technologies [36][37].

以下文章来源于3D视觉之心 ，作者3D视觉之心 3D视觉之心 . 3D视觉与SLAM、点云相关内容分享 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15个 方向 学习 路线 评估协议的关键局限性 三维计算机视觉领域高度关注于捕捉场景的几何和视觉外观，以及理解其内容。近年来，三维高斯溅射（3D Gaussian Splatting, 3DGS）因其独特的能力——能够以一种紧凑的形式联合编码场景的几何、外观和理解属性 （该形式可以有效地从二维带位姿的图像中优化得到）——已成为最理想的三维表示方法。此外，视觉-语言推 理代表了三维场景理解最具前景的方向，因为它将场景的视觉和几何属性与我们用来定义、描述和推理概念的语 言连接起来。因此，本文专注于利用 3DGS 进行视觉-语言场景理解。 语言高斯溅射（Language Gaussian Splatting, LGS）最相关的方法可分为三类。前两类方法首先使用视觉-语言基 础模型（例如 CLIP）从所有训练图像中提取二维特征。第一类随后执行基于梯度的单场景优化，将特征向量分 配给每个三维高斯基元（primitive），并优化它们，使其渲染 ...