Core Insights - The article discusses the rising importance of 3DGS (3D Geometry Synthesis) technology in various fields, particularly in autonomous driving, healthcare, virtual reality, and gaming [2][4] - A comprehensive learning roadmap for 3DGS has been developed to address the industry's need for effective training in scene reconstruction and world modeling [4][6] Course Overview - The course titled "3DGS Theory and Algorithm Practical Tutorial" aims to provide a detailed understanding of 3DGS algorithms, covering both theoretical foundations and practical applications [6][10] - The course is designed in six chapters, starting from basic knowledge to advanced research directions in 3DGS [10][11] Chapter Summaries - **Chapter 1: Background Knowledge** Introduces foundational concepts in computer graphics, including implicit and explicit representations of 3D space, rendering pipelines, and tools like SuperSplat and COLMAP [10][11] - **Chapter 2: Principles and Algorithms** Focuses on the core principles of 3DGS, including dynamic and surface reconstruction, and introduces the 3DGRUT framework for practical learning [11][12] - **Chapter 3: 3DGS in Autonomous Driving** Highlights key works in the field, such as Street Gaussian and OmniRe, and utilizes DriveStudio for practical applications [12][13] - **Chapter 4: Important Research Directions** Discusses significant research areas like COLMAP extensions and depth estimation, emphasizing their relevance to both industry and academia [13][14] - **Chapter 5: Feed-Forward 3DGS** Explores the development and principles of feed-forward 3DGS, including recent algorithms like AnySplat and WorldSplat [14][15] - **Chapter 6: Q&A Discussion** Provides a platform for participants to discuss industry pain points and job demands related to 3DGS [15] Target Audience and Learning Outcomes - The course is aimed at individuals with a background in computer graphics, visual reconstruction, and programming, particularly those interested in pursuing careers in the 3DGS field [19][17] - Participants will gain comprehensive knowledge of 3DGS theory, algorithm development frameworks, and opportunities for networking with industry professionals [19][17]

Core Insights - The article discusses the implications of OpenAI's new video generation model, Sora, on computer graphics, particularly in relation to 3D Gaussian Splatting (3DGS) and its potential to replace traditional rendering techniques [7][8]. Group 1: 3D Gaussian Splatting (3DGS) - 3DGS is highlighted as a significant area of research, with ongoing developments in its application for self-driving perception and scene reconstruction [4][9]. - The gsplat library is recommended for its better documentation and maintenance compared to the original Gaussian Splatting library, indicating a preference for more user-friendly resources in the field [5]. - The article mentions the potential for 3DGS to integrate with other technologies, such as NeRF (Neural Radiance Fields), to enhance video generation and scene understanding [4][9]. Group 2: Technical Aspects of Sora and 3DGS - Sora's capabilities are positioned as a potential game-changer in computer graphics, with the possibility of it being recognized as a foundational technology in the field [6][7]. - The article outlines various technical components of 3DGS, including the use of Gaussian parameters, covariance matrices, and the importance of camera coordinate transformations [21][22][30]. - The compression capabilities of gsplat are noted, with the ability to reduce Gaussian parameters significantly while maintaining quality, which is crucial for efficient rendering [13][14]. Group 3: Future Prospects and Community Engagement - The article expresses optimism about the broader application of "world models" in video generation and scene reconstruction, suggesting that even smaller players in the industry could benefit from advancements in these technologies [9]. - The community around autonomous driving and related technologies is emphasized, with numerous technical groups and resources available for learning and collaboration [78].

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].

Core Insights - The article discusses the advancements in 3D scene generation, highlighting a comprehensive survey that categorizes existing methods into four main paradigms: procedural methods, neural network-based 3D representation generation, image-driven generation, and video-driven generation [2][4][7]. Summary by Sections Overview of 3D Scene Generation - A survey titled "3D Scene Generation: A Survey" reviews over 300 representative papers and outlines the rapid growth in the field since 2021, driven by the rise of generative models and new 3D representations [2][4][5]. Four Main Paradigms - The four paradigms provide a clear technical roadmap for 3D scene generation, with performance metrics compared across dimensions such as realism, diversity, viewpoint consistency, semantic consistency, efficiency, controllability, and physical realism [7]. Procedural Generation - Procedural generation methods automatically construct complex 3D environments using predefined rules and constraints, widely applied in gaming and graphics engines. This category can be further divided into neural network-based generation, rule-based generation, constraint optimization, and large language model-assisted generation [8]. Image-based and Video-based Generation - Image-based generation leverages 2D image models to reconstruct 3D structures, while video-based generation treats 3D scenes as sequences of images, integrating spatial modeling with temporal consistency [9]. Challenges in 3D Scene Generation - Despite significant progress, challenges remain in achieving controllable, high-fidelity, and physically realistic 3D modeling. Key issues include uneven generation capabilities, the need for improved 3D representations, high-quality data limitations, and a lack of unified evaluation standards [10][16]. Future Directions - Future advancements should focus on higher fidelity generation, parameter control, holistic scene generation, and integrating physical constraints to ensure structural and semantic consistency. Additionally, supporting interactive scene generation and unifying perception and generation capabilities are crucial for the next generation of 3D modeling systems [12][18].