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 ...

对于从事自动化和计算机的同学，建议搞深度学习，VLA、端到端、世界模型都是很好的方向，从入门、到 工作甚至读博都有很大空间。对于机械和车辆的同学，可以先学习传统PnC、3DGS这些方向算力低、入手简 单。 剩下的就是一些方法论的提升了，多看论文多交流，慢慢形成自己的思考和idea。 对很多新人研究者，一个 好的idea需要踩很多次坑。如果你还是新人，不知道怎么入门，可以看看我们推出的论文辅导。 论文辅导上线了！ 端到端、VLA、世界模型、强化学习、3D目标检测、多传感器融合、3DGS、BEV感知、Occupancy Network、多任务学习、语义分割、轨迹预测、运动规划、扩散模型、Flow matching、点云感知、毫米波雷 达、单目感知、车道线/在线高精地图等方向。 如果您有任意论文发表需求，支持带课题/研究方向咨询，欢迎联系我们， 微信：paperguidance 提供的服务 论文选题； 论文全流程指导； 实验指导； 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 最近收到不少同学的咨询，很多都是计算机、车辆、自动化和机械方向的同学。 先看自驾一些 ...

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 这两天有小伙伴在群里抛出这个问题，非常有建设性，分享给大家？ 探讨feed-forward GS在自驾场景落地的难点目前在哪里？ 目前来看Feed-forward的相关方法在点云精度还是差一点的，尤其是ff的方法在私有数据的域上精度不稳定。前馈方法的前景是广阔的，毕竟克服了per scene优化 的弊端，值得持续尝试预研和落地。 关于3DGS相关的技术栈，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。 第二章则正式进入到3DGS的原理和算法部分。 整体上第二章的设计思路是带大家先打好基础，先详细梳理3DGS的原理部分及核心伪代码，接着讲解动态重建、 表面重建、鱼眼重建和光线追踪的经典文章和最新的算法，由点及面层层深入。实战我们选取了英伟达开源的3DGRUT框架，适合 ...

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 discusses various advanced directions in autonomous driving research, emphasizing the importance of deep learning and traditional methods for different academic backgrounds [2][3]. Group 1: Research Directions - Key areas of focus include VLA, end-to-end learning, reinforcement learning, 3DGS, and world models, which are recommended for students in computer science and automation [2]. - For mechanical and vehicle engineering students, traditional methods like PnC and 3DGS are suggested due to their lower computational requirements and ease of entry [2]. Group 2: Paper Guidance Services - The article announces the launch of a paper guidance service that covers various topics such as end-to-end learning, multi-sensor fusion, and trajectory prediction [3][6]. - The service includes support for topic selection, full process guidance, and experimental assistance [6]. Group 3: Publication Success - The guidance service has a high acceptance rate for papers submitted to top conferences and journals, including CVPR, AAAI, and ICLR [7]. - The article highlights the range of publication venues, including CCF-A, CCF-B, and various SCI categories [10].

Core Insights - Moore Threads won the silver medal at the 3D Gaussian Splatting Reconstruction Challenge during SIGGRAPH Asia 2025, showcasing its advanced algorithm capabilities and hardware-software optimization in next-generation graphics rendering technology [1][16]. Group 1: 3D Gaussian Splatting Technology - 3D Gaussian Splatting (3DGS) is a revolutionary 3D scene representation and rendering technology that achieves an exceptional balance between image quality, efficiency, and resource usage, significantly outperforming traditional NeRF methods by enhancing rendering efficiency by hundreds to thousands of times [4][19]. - 3DGS has shown strong adaptability and scalability in areas such as ray tracing, real-time VR/AR rendering, and multi-modal fusion, making it a key technology in the evolving landscape of graphics rendering [4][8]. Group 2: Competition Overview - The 3DGS Reconstruction Challenge required participants to complete high-quality 3DGS reconstruction within 60 seconds using provided real terminal video sequences and SLAM point clouds, emphasizing both reconstruction quality and speed [10][12]. - The evaluation metrics included PSNR (Peak Signal-to-Noise Ratio) and reconstruction speed, ensuring a fair and authoritative ranking of the competing teams [12]. Group 3: Performance Results - Moore Threads' team, identified as "MT-AI," achieved an average PSNR of 27.58 and a reconstruction time of 34 seconds, placing them third overall in the competition [17][20]. - The results highlighted the company's leading capabilities in 3DGS algorithm construction and hardware-software optimization [16][20]. Group 4: LiteGS Development - Moore Threads developed the LiteGS library, which optimizes the entire pipeline from GPU systems to data management and algorithm design, achieving a training acceleration of up to 10.8 times while reducing parameter count by over 50% [20][25]. - LiteGS has been open-sourced on GitHub to promote collaboration and continuous evolution in 3D reconstruction and rendering technologies [27]. Group 5: Strategic Implications - The success at the SIGGRAPH Asia competition reflects Moore Threads' strategic understanding of global technology trends and its ability to lead future graphics computing directions [28]. - The advancements in 3DGS technology highlight the high demands for algorithm and hardware collaboration, positioning Moore Threads as a forward-thinking player in the graphics intelligence computing field [28].

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]

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 智驾的技术焦虑，正在中游厂商快速传播。 周末有机会和一位深耕主机厂L2量产交付的负责人线下交流，其认为 明年才是端到端等前沿技术大规模量产的起点。 智驾前沿的技术发展放缓，业内量产方案趋同，L2整体在走下沉路线。二十万以上的乘用车销量在700万左右，但头部新势力的销量不及1/3，更不用说端到端量产 占比的车型。从落地趋势上来看，端到端技术的成熟反而才是更大规模量产的开端。随着明年L3法规的进一步推进， 中游厂商的技术升级也是迫在眉睫。 所以这 两个月很多公司算法负责人联系自动驾驶之心，迫切的想要了解前沿的技术：端到端、世界模型、VLA、3DGS等等。 端到端不仅仅是一个算法，需要完善的云端&车端基建，数据闭环、工程部署、闭环测试、模型优化、平台开发等等，可以预见，中阶智能驾驶的岗位需求会更旺 盛。而在昨天的2025地平线技术生态大会上，地平线CEO也表示将挺进10万级市场，高阶智驾正在迅速下沉至更多的国民车型。明年，智能驾驶的故事将更精彩。 以上。 基本上可以判断端到端、VLA的招聘需求会更旺盛。最近几个月， ...

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].

Core Insights - The article discusses the rapid advancements in 3D Generative Synthesis (3DGS) technology, highlighting its applications in various fields such as 3D modeling, virtual reality, and autonomous driving simulation [2][4] - A comprehensive learning roadmap for 3DGS has been developed to assist newcomers in mastering both theoretical and practical aspects of the technology [4][6] Group 1: 3DGS Technology Overview - The core goal of new perspective synthesis in machine vision is to create 3D models from images or videos that can be processed by computers, leading to numerous applications [2] - The evolution of 3DGS technology has seen significant improvements, including static reconstruction (3DGS), dynamic reconstruction (4DGS), and surface reconstruction (2DGS) [4] - The introduction of feed-forward 3DGS has addressed the inefficiencies of per-scene optimization methods, making the technology more accessible [4][14] Group 2: Course Structure and Content - The course titled "3DGS Theory and Algorithm Practical Tutorial" covers detailed explanations of 2DGS, 3DGS, and 4DGS, along with important research topics in the field [6] - The course is structured into six chapters, starting from foundational knowledge in computer graphics to advanced topics like feed-forward 3DGS [10][11][14] - Each chapter includes practical assignments and discussions to enhance understanding and application of the concepts learned [10][15] Group 3: Target Audience and Prerequisites - The course is designed for individuals with a background in computer graphics, visual reconstruction, and programming, particularly in Python and PyTorch [19] - Participants are expected to have a GPU with a recommended computing power of 4090 or higher to effectively engage with the course material [19] - The course aims to benefit those seeking internships, campus recruitment, or job opportunities in the field of 3DGS [19]