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 - A new algorithm, RegGS, developed by the Hong Kong University of Science and Technology (Guangzhou), can reconstruct 3D models from sparse 2D images without precise camera positioning, achieving centimeter-level accuracy suitable for VR applications [2][4]. Group 1: Methodology - RegGS combines feed-forward Gaussian representation with structural registration to address the challenges of sparse and pose-less images, providing a new pathway for practical 3D reconstruction [6][8]. - The core mechanism involves registering local 3D Gaussian mixture models to gradually build a global 3D scene, avoiding reliance on traditional Structure from Motion (SfM) initialization and requiring fewer input images [8][12]. Group 2: Experimental Results - In experiments on the RE10K and ACID datasets, RegGS outperformed existing mainstream methods across various input frame counts (2×/8×/16×/32×) in metrics such as PSNR, SSIM, and LPIPS [9][12]. Group 3: Applications - RegGS addresses the "sparse + pose-less" problem with significant real-world applications, including: - 3D reconstruction from user-generated content (UGC) videos, which often lack camera parameters [13]. - Drone aerial mapping, demonstrating robustness to large viewpoint variations and low frame rates [13]. - Restoration of historical images/documents, enabling 3D reconstruction from a few photos taken from different angles [13]. - Compared to traditional SfM or Bundle Adjustment methods, RegGS requires less structural input and is more feasible for unstructured data applications [13]. Group 4: Limitations and Future Directions - The performance and efficiency of RegGS are currently limited by the quality of the upstream feed-forward model and the computational cost of the MW2 distance calculation, indicating areas for future optimization [13].

Core Viewpoint - The article emphasizes the significance of high-fidelity simulation technology in overcoming the challenges of testing autonomous driving algorithms, particularly through the introduction of New View Synthesis (NVS) technology, which allows for the creation of closed-loop driving simulation environments based on real-world data [1][2]. Group 1: Challenges and Tasks - The workshop addresses two main challenges in the application of NVS technology, focusing on the need for improved rendering quality in extrapolated views and the evaluation of driving algorithms in closed-loop simulation environments [2][3]. - The first track, "Extrapolated View New View Synthesis," aims to enhance rendering quality under sparse input views, which is crucial for evaluating autonomous driving algorithms in various scenarios [3][4]. - The second track, "Closed-Loop Simulation Evaluation," highlights the importance of creating high-fidelity simulation environments that bridge the gap between real-world data and interactive assessments, overcoming the limitations of traditional static datasets [5][6]. Group 2: Competition Details - Each track of the workshop offers awards, including a Creative Award of $9,000, and the competition is set to commence on June 30, 2025, with submissions due by August 31, 2025 [8][9]. - The workshop encourages global participation to advance autonomous driving technology, providing a platform for challenging and valuable research [10][11].