Core Viewpoint - The article discusses the technological anxiety in intelligent driving, particularly among mid-tier manufacturers, and highlights the anticipated growth in demand for end-to-end (E2E) and VLA (Vision-Language-Action) technologies in the coming year [2]. Group 1: Industry Trends - The mass production of cutting-edge technologies like end-to-end systems is expected to begin next year, with L2 technology becoming more standardized and moving towards lower-tier markets [2]. - The total sales of passenger vehicles priced above 200,000 are around 7 million, but leading new forces account for less than one-third of this, indicating a slow adoption of end-to-end mass production models [2]. - The maturity of end-to-end technology is seen as a precursor to larger-scale production, with the advancement of L3 regulations prompting urgent upgrades among mid-tier manufacturers [2]. Group 2: Recruitment and Training - There is a growing demand for positions related to end-to-end and VLA technologies, as many professionals are seeking to quickly learn these advanced skills [3]. - The article mentions the launch of specialized courses aimed at practical applications of end-to-end and VLA technologies, designed for individuals already working in the field [3][6]. - The courses will cover various modules, including navigation information application, reinforcement learning optimization, and production experiences related to diffusion and autoregressive models [3][6]. Group 3: Course Details - The end-to-end production course will focus on practical implementation, including seven major practical applications, making it suitable for those looking to advance their careers [3][6]. - The VLA course will cover foundational algorithms and theories, including BEV perception and large language models, with practical projects based on diffusion models and VLA algorithms [6][11]. - The instructors for these courses are experienced professionals from top-tier companies and academic institutions, ensuring a high-quality learning experience [5][8][13].

Core Insights - The article discusses the development and application of 3D Gaussian Splatting (3DGS) technology, emphasizing its significance in the field of autonomous driving and 3D reconstruction [3][9]. Group 1: Course Overview - The course titled "3DGS Theory and Algorithm Practical Tutorial" aims to provide a comprehensive learning roadmap for 3DGS, covering both theoretical and practical aspects [3][6]. - The course is designed for individuals interested in entering the 3DGS field, focusing on essential concepts such as point cloud processing and deep learning [3][6]. Group 2: Course Structure - Chapter 1 introduces foundational knowledge in computer graphics, including implicit and explicit representations of 3D space, rendering pipelines, and tools like SuperSplat and COLMAP [6][7]. - Chapter 2 delves into the principles and algorithms of 3DGS, covering dynamic reconstruction and surface reconstruction, with practical applications using the NVIDIA open-source 3DGRUT framework [7][8]. - Chapter 3 focuses on the application of 3DGS in autonomous driving simulations, highlighting key works and tools like DriveStudio for practical learning [8][9]. - Chapter 4 discusses important research directions in 3DGS, including COLMAP extensions and depth estimation, along with insights on their industrial and academic relevance [9][10]. - Chapter 5 covers Feed-Forward 3DGS, detailing its development and algorithmic principles, including recent works like AnySplat and WorldSplat [10]. - Chapter 6 provides a platform for Q&A and discussions on industry demands and challenges related to 3DGS [11]. Group 3: Target Audience and Requirements - The course is aimed at individuals with a background in computer graphics, visual reconstruction, and familiarity with technologies like NeRF and 3DGS [15]. - Participants are expected to have a basic understanding of probability theory, linear algebra, and proficiency in Python and PyTorch [15].

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。 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。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。 早鸟优惠！名额仅限「30名」 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，拥有丰富的三维重建战经验。 课程大纲 这门课程讲如何展开 第一章：3DGS的背景知识 第一章主要 ...

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]

Core Insights - The article discusses a novel approach to reinforcement learning (RL) for end-to-end (e2e) policy development in autonomous driving, utilizing 3D Graphics Simulation (3DGS) to enhance training environments [1][2] - The proposed method significantly reduces collision rates, achieving a threefold decrease compared to pure imitation learning (IL) [1] - Limitations of the 3DGS environment include a lack of interaction, reliance on log replay, and inadequate rendering of non-rigid pedestrians and low-light scenarios [1] Summary by Sections Methodology - The approach consists of three main phases: training a basic Bird's Eye View (BEV) and perception model, freezing perception to train a planning head using IL, and generating a sensor-level environment with 3DGS for mixed training of RL and IL [3][5][6] - The training process involves pre-training perception models, followed by IL training on human expert data, and finally fine-tuning with RL to enhance sensitivity to critical risk scenarios [10][12] State and Action Space - The state space includes various encoders for BEV features, static map elements, traffic participant information, and planning-related features [7] - The action space is defined with discrete movements for lateral and longitudinal actions, allowing for a total of 61 actions in both dimensions [8] Reward Function - The reward function is designed to penalize collisions and deviations from expert trajectories, with specific thresholds for dynamic and static collisions, as well as positional and heading deviations [17][19] - Auxiliary tasks are introduced to stabilize training and accelerate convergence, focusing on behaviors like deceleration and acceleration [20][23] Experimental Results - The results indicate that the proposed method outperforms other IL-based algorithms, demonstrating the advantages of closed-loop training in dynamic environments [28][29] - The optimal ratio of RL to IL data is found to be 4:1, contributing to improved performance metrics [28] Conclusion - The article emphasizes the practical engineering improvements achieved through the integration of 3DGS in training environments, leading to better performance in autonomous driving applications [1][2]

Core Insights - The rapid technological iteration in 3DGS (3D Graphics Systems) is highlighted, with advancements from static reconstruction to dynamic and surface reconstruction, culminating in feed-forward 3DGS [1] - A comprehensive learning roadmap for 3DGS has been developed to assist newcomers in mastering both theoretical and practical aspects of the technology [1] Course Overview - The course is structured into six chapters, starting with foundational knowledge in computer graphics and progressing through principles, algorithms, and applications in autonomous driving [5][6][7][8][9] - The course aims to provide a detailed understanding of 3DGS, including tools like SuperSplat and frameworks such as Gsplat and DriveStudio [5][6][7] Target Audience - The course is designed for individuals with a background in computer graphics, visual reconstruction, and programming, specifically those familiar with Python and PyTorch [14] Learning Outcomes - Participants will gain a solid grasp of 3DGS theory, algorithm development, and industry applications, enabling them to engage in discussions about job demands and industry challenges [10][12]

Core Viewpoint - The article emphasizes the advancements in Tesla's world model and its implementation of FeedForward GS, which significantly enhances the efficiency and accuracy of 3D scene reconstruction compared to traditional methods [2][4]. Group 1: Tesla's Technological Advancements - Tesla utilizes FeedForward GS to create 3D scenes directly from visual inputs, reducing optimization time from 30 minutes to 220 milliseconds, eliminating reliance on point cloud initialization [4]. - The comparison between traditional GS and Tesla's generative GS shows substantial improvements in dynamic target clarity and artifact reduction, indicating a strong competitive edge for Tesla in the autonomous driving sector [4]. Group 2: Industry Implications - The advancements made by Tesla are likely to prompt domestic competitors to enhance their capabilities, leading to increased demand for related job positions in the industry [4][6]. - The rapid iteration of 3DGS technology is attracting attention in both academic and industrial circles, highlighting the need for effective learning pathways for newcomers in the field [7]. Group 3: Educational Initiatives - An educational program titled "3DGS Theory and Algorithm Practical Tutorial" has been developed to provide a comprehensive learning roadmap for 3DGS technology, covering everything from foundational theories to practical applications [7]. - The course includes various chapters focusing on background knowledge, principles and algorithms, autonomous driving applications, important research directions, and the latest developments in Feed-Forward 3DGS [11][12][13][14][15]. Group 4: Course Structure and Requirements - The course is structured to span approximately two and a half months, with specific unlock dates for each chapter, allowing participants to progress systematically [18]. - Participants are required to have a GPU with a recommended capability of 4090 or higher, along with a foundational understanding of computer graphics, visual reconstruction, and relevant programming skills [20].

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 and practical [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 with foundational knowledge in computer graphics and progressing to advanced topics such as feed-forward 3DGS [10][11][14] - Each chapter includes practical assignments and discussions to enhance understanding and application of the concepts learned [10][12][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]

Core Viewpoint - The article discusses the innovative approach of DrivingGaussian++, a framework developed by researchers from Peking University and Google DeepMind, which enables realistic reconstruction and editable simulation of dynamic driving scenes without the need for extensive training [4][18]. Group 1: Importance of Data in Autonomous Driving - Data diversity and quality are crucial for the performance and potential of models in autonomous driving, with a focus on addressing the long-tail scenarios that are often underrepresented in datasets [2][3]. - The emergence of 3D scene editing as a specialized field aims to enhance the robustness and safety of autonomous driving systems by simulating various real-world driving conditions [2]. Group 2: Challenges in 3D Scene Editing - Existing editing tools often specialize in one aspect of 3D scene editing, leading to inefficiencies when applied to large-scale autonomous driving simulations [3]. - Accurate reconstruction of 3D scenes is challenging due to limited sensor data, high-speed vehicle movement, and varying lighting conditions, making it difficult to create a complete and realistic 3D environment [3][13]. Group 3: DrivingGaussian++ Framework - DrivingGaussian++ utilizes a composite Gaussian splatting approach to layer model complex driving scenes, separating static backgrounds from dynamic targets for more precise reconstruction [4][6]. - The framework introduces novel modules, including Incremental Static 3D Gaussians and Composite Dynamic Gaussian Graphs, to enhance the modeling of both static and dynamic elements in driving scenes [6][31]. Group 4: Editing Capabilities - The framework allows for controlled and efficient editing of reconstructed scenes without additional training, covering tasks such as texture modification, weather simulation, and target manipulation [20][41]. - By integrating 3D geometric priors and leveraging large language models for dynamic predictions, the framework ensures coherence and realism in the editing process [41][51]. Group 5: Performance Comparison - DrivingGaussian++ outperforms existing methods in terms of visual realism and quantitative consistency across various editing tasks, demonstrating superior performance in dynamic driving scenarios [62][70]. - The editing time for DrivingGaussian++ is significantly lower than that of other models, typically ranging from 3 to 10 minutes, highlighting its efficiency [70].

Core Viewpoint - The article emphasizes the establishment of a leading communication platform for autonomous driving technology in China, focusing on industry, academic, and career development aspects [1]. Group 1 - The platform, named "Autonomous Driving Heart," aims to facilitate discussions and exchanges among professionals in various fields related to autonomous driving technology [1]. - The technical discussion group covers a wide range of topics including large models, end-to-end systems, VLA, BEV perception, multi-modal perception, occupancy, online mapping, 3DGS, multi-sensor fusion, transformers, point cloud processing, SLAM, depth estimation, trajectory prediction, high-precision maps, NeRF, planning control, model deployment, autonomous driving simulation testing, product management, hardware configuration, and AI job exchange [1]. - Interested individuals are encouraged to join the community by adding a WeChat assistant and providing their company/school, nickname, and research direction [1].