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].

Core Algorithm - The industry has shifted towards end-to-end solutions, moving away from modular approaches, at least in public discourse [1] - The introduction of world models is prevalent, with some companies using them to generate training data, while others incorporate them into end-to-end models to enhance performance [1][8] - There is a divergence in opinions regarding the necessity of language models (VLA) in autonomous driving, with some companies arguing that language is not essential for driving tasks [1][11] Simulation and Infrastructure - The closed-loop systems have evolved from data-driven to simulation testing and training loops [2] - 3DGS is highlighted as a crucial technology for building simulation environments, as emphasized by Tesla at CVPR 2025 [5] - Infrastructure is critical, with companies like Xiaomi and Li Auto noting its benefits for development efficiency [3][14] Organizational Capability - Organizational ability is vital, as large autonomous driving teams face significant management challenges [4] - Team culture and collaboration are emphasized as essential for overcoming complex technical and management issues [5] Technical Choices Comparison - A comparison of various companies' technical choices reveals differing approaches to core technologies and the role of world models and simulation tools [9] - Companies like Li Auto advocate for a training loop that evolves from imitation to self-learning, while NVIDIA emphasizes interpretability and reasoning in AI [9] Key Non-Core Factors - R&D infrastructure and engineering efficiency are crucial for the success of autonomous driving technologies [14] - Simulation and synthetic data are becoming essential for addressing corner cases that real-world data cannot cover [14] - The scale of computing power and chip adaptation is critical, as autonomous driving is not just a software issue but also a hardware challenge [15] User Experience and Safety - User experience and safety are paramount, with companies like Xiaomi stressing the importance of balancing advanced technology with user concerns [17] - The need for a dual-stack safety mechanism is highlighted, ensuring that even aggressive end-to-end models have a fallback to traditional rule-based systems for safety [19]

Core Viewpoint - Elon Musk has decided to phase out the one-time purchase option for Tesla's Full Self-Driving (FSD) by February 14, 2026, favoring a Software as a Service (SaaS) model instead [1]. Pricing and Market Strategy - In the U.S., the one-time purchase price for FSD is $8,000, while the monthly subscription price will decrease to $99, making the subscription equivalent to a purchase over 81 months. In China, the buyout price is approximately 64,000 RMB, with the subscription model expected to lower the barrier for adoption and increase subscription rates [2]. - Reports indicate that FSD has received "partial approval" in China, with full approval anticipated around February or March 2026. The monthly subscription fee in China is projected to be between 499 and 699 RMB [2]. Technological Developments - Tesla's FSD continues to utilize an end-to-end Variational Autoencoder (VA) architecture, with ongoing optimizations. The focus is on user acceptance and engineering improvements, indicating a challenging period ahead for autonomous driving [3]. - Recent advancements in FSD include the development of 3D Gaussian closed-loop simulation capabilities, which are expected to enhance action optimization [2]. Future Outlook - The company is also making strides with its Optimus V3 project, which is anticipated to be a transformative technology, potentially overshadowing Tesla's automotive legacy [3].

Core Insights - The article emphasizes the importance of deep learning in the fields of automation and computer science, particularly for students in these areas to explore cutting-edge topics such as VLA, end-to-end learning, and world models [2][3] - It highlights the need for newcomers to engage with research papers and discussions to develop their own ideas and methodologies [2] - The article introduces a paper guidance service aimed at assisting students with various aspects of research paper writing and publication [3][4][6] Group 1 - The article suggests that students from computer science and automation backgrounds should focus on deep learning, with specific recommendations for topics like VLA, end-to-end learning, and world models [2] - For mechanical and vehicle engineering students, it recommends starting with traditional PnC and 3DGS due to their lower computational requirements and ease of entry [2] - The article encourages new researchers to learn from failures and emphasizes the importance of developing personal insights through extensive reading and communication [2] Group 2 - The paper guidance service offers support in selecting research topics, full process guidance, and experimental assistance [6] - The service has a high acceptance rate for papers submitted to top conferences and journals, including CVPR, AAAI, and ICLR [7] - Pricing for the guidance service varies based on the level of the paper, and further details can be obtained by contacting the research assistant [8]

Core Viewpoint - The article emphasizes the significance of the development of 3D Gaussian Splatting (3DGS) in the field of autonomous driving, highlighting its potential to enhance simulation capabilities and improve the efficiency of scene reconstruction [2][3]. Group 1: Development and Importance of 3DGS - The introduction of 3D Gaussian Splatting (3DGS) is seen as a major advancement, with Tesla's recent sharing indicating a shift towards end-to-end and generative approaches in autonomous driving [2]. - The evolution of 3DGS is outlined as a progression from static reconstruction to dynamic and mixed scene reconstruction, culminating in the feed-forward GS approach [3]. Group 2: Course Overview and Structure - A comprehensive course on 3DGS has been developed, covering theoretical foundations and practical applications, designed to aid beginners in understanding the complexities of the technology [3][8]. - The course is structured into six chapters, each focusing on different aspects of 3DGS, including background knowledge, principles and algorithms, and important research directions [8][9][10][11][12]. Group 3: Technical Highlights - Key features of the 3DGS approach include a unified network architecture that enhances training, inference, and testing, achieving real-time performance at a hundred milliseconds level [6]. - The integration of world models with 3DGS allows for improved closed-loop simulation capabilities, combining generation and reconstruction [6]. Group 4: Target Audience and Learning Outcomes - The course is aimed at individuals with a foundational understanding of computer graphics, visual reconstruction, and programming, providing them with the skills necessary for careers in both academia and industry [17]. - Participants will gain a thorough understanding of 3DGS theory, algorithm development frameworks, and the ability to engage with peers in the field [17].

Core Insights - The article discusses various cutting-edge directions in autonomous driving research, emphasizing the importance of deep learning and traditional methods for students in related fields [2][3]. Group 1: Research Directions - Key areas of focus include VLA, end-to-end learning, reinforcement learning, 3D goal detection, and occupancy networks, which are recommended for students in computer science and automation [2][3]. - For mechanical and vehicle engineering students, traditional methods like PnC and 3DGS are suggested as they require lower computational power and are easier to start with [2]. Group 2: Guidance and Support - The article announces the launch of a paper guidance service that offers support in various research areas, including multi-sensor fusion, trajectory prediction, and semantic segmentation [3][6]. - Services provided include topic selection, full process guidance, and experimental support, aimed at enhancing the research capabilities of students [6][7]. Group 3: Publication Opportunities - 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 availability of support for various publication levels, including CCF-A, CCF-B, and SCI indexed journals [10].

Core Viewpoint - The article discusses the challenges and advancements in the field of 3D Generative Synthesis (3DGS) for autonomous driving, emphasizing the importance of a structured learning path for newcomers in the industry [2][6]. 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 foundations and practical applications [2][6]. - The course is designed in collaboration with industry algorithm experts and spans over two and a half months, starting from December 1 [13]. Group 2: Course Structure - Chapter 1 introduces the background knowledge of 3DGS, including basic concepts of computer graphics, implicit and explicit representations of 3D space, and common development tools like SuperSplat and COLMAP [6][7]. - Chapter 2 delves into the principles and algorithms of 3DGS, covering dynamic reconstruction, surface reconstruction, and ray tracing, with practical exercises using the NVIDIA open-source 3DGRUT framework [7][8]. - Chapter 3 focuses on the application of 3DGS in autonomous driving simulation, highlighting key works and practical tools like DriveStudio for further learning [8][9]. - Chapter 4 discusses important research directions in 3DGS, including extensions of COLMAP and depth estimation, and their relevance to both industry and academia [9]. - Chapter 5 covers Feed-Forward 3DGS, detailing its development history and algorithmic principles, along with discussions on recent algorithms like AnySplat and WorldSplat [10]. Group 3: Interaction and Support - Chapter 6 is dedicated to online discussions and Q&A sessions, allowing participants to engage with instructors on industry pain points and job market demands [11]. - The course encourages continuous interaction between students and professionals from both academia and industry, enhancing networking opportunities [15].

Core Viewpoint - The article discusses the advancements in 3D Gaussian Splatting (3DGS) technology in the autonomous driving sector, highlighting the introduction of feed-forward GS algorithms and the need for effective learning pathways for newcomers in the field [2][4]. Group 1: Course Overview - A new course titled "3DGS Theory and Algorithm Practical Tutorial" has been developed to provide a comprehensive learning roadmap for 3DGS technology, covering both theoretical and practical aspects [4]. - The course is designed to help participants understand point cloud processing, deep learning theories, real-time rendering, and coding practices [4]. Group 2: Course Structure - The course consists of six chapters, starting with foundational knowledge in computer graphics and moving through the principles and algorithms of 3DGS, including dynamic and surface reconstruction [8][9]. - The third chapter focuses on the application of 3DGS in autonomous driving simulation, providing insights into key works and tools used in the industry [10]. - Subsequent chapters explore important research directions in 3DGS, including COLMAP extensions and depth estimation, as well as the emerging feed-forward 3DGS techniques [11][12]. 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 [17]. - Participants are expected to have access to a GPU with a recommended capability of 4090 or higher to effectively engage with the course content [17].

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].