Core Insights - The article highlights the significant achievement of Moore Threads at the SIGGRAPH Asia 2025, where the company won a silver medal in the 3D Gaussian Splatting Reconstruction Challenge, showcasing its advanced algorithm capabilities and hardware-software optimization in next-generation graphics rendering technology [1][17]. Group 1: 3D Gaussian Splatting Technology - 3D Gaussian Splatting (3DGS) is a revolutionary 3D scene representation and rendering technology introduced in 2023, achieving a remarkable balance between image quality, efficiency, and resource usage, with rendering efficiency improved by hundreds to thousands of times compared to traditional NeRF [4][8]. - The technology demonstrates strong adaptability and scalability in areas such as ray tracing, real-time VR/AR rendering, and multimodal fusion, making it a foundational technology for embodied AI, which requires high-quality, low-latency 3D environment modeling [7][8]. Group 2: Competition Details - The 3DGS Reconstruction Challenge required participants to complete high-quality 3DGS reconstruction within 60 seconds using real terminal video sequences and imperfect camera trajectories, emphasizing the challenge of achieving both reconstruction quality and speed [10][12]. - The evaluation metrics included PSNR (Peak Signal-to-Noise Ratio) for reconstruction quality and time taken, ensuring a fair and transparent ranking process [12][14]. Group 3: Moore Threads' Performance - Moore Threads' AI team, competing under the identifier "MT-AI," achieved a commendable balance in reconstruction accuracy and efficiency, securing the second place with an average PSNR of 27.58 and a reconstruction time of 34 seconds [17][21]. - The results from the competition indicated that Moore Threads' performance was competitive, with the top team achieving a PSNR of 28.43 and a reconstruction time of 57 seconds [18]. Group 4: LiteGS Library - Moore Threads developed the LiteGS library, which optimizes the entire pipeline from GPU systems to data management and algorithm design, achieving a PSNR of 27.58 and a reconstruction time of 34 seconds, significantly ahead of many competitors [21][24]. - LiteGS can achieve up to 10.8 times training acceleration while reducing parameter count by over 50%, demonstrating its engineering practicality and technological foresight [25][31]. - The library has been fully open-sourced on GitHub to promote collaborative development and continuous evolution in 3D reconstruction and rendering technology [27].