NGM-SLAM：基于辐射场子地图的高斯溅射SLAM
NGM-SLAM: Gaussian Splatting SLAM with Radiance Field Submap
AI总结：
NGM-SLAM是首个利用神经辐射场子地图进行渐进式场景表达的3DGS SLAM系统，通过融合子地图的高斯渲染实现高质量场景表达和在线回环调整，支持多种输入，达到最先进的场景重建和跟踪性能。
摘要：
基于高斯溅射的SLAM系统因其快速实时渲染和高保真建图能力而受到关注。然而，当前的高斯溅射SLAM系统通常难以处理大规模场景表示，且缺乏有效的回环检测。为解决这些问题，我们提出了NGM-SLAM，这是首个利用神经辐射场子地图进行渐进式场景表达的3DGS SLAM系统，有效整合了神经辐射场和3D高斯溅射的优势。我们利用神经辐射场子地图作为监督，通过融合子地图的高斯渲染实现高质量场景表达和在线回环调整。我们在多个真实场景和大规模场景数据集上的结果表明，我们的方法能够实现准确的空洞填充和高质量场景表达，支持单目、立体和RGB-D输入，并达到最先进的场景重建和跟踪性能。
英文摘要：
SLAM systems based on Gaussian Splatting have garnered attention due to their capabilities for rapid real-time rendering and high-fidelity mapping. However, current Gaussian Splatting SLAM systems usually struggle with large scene representation and lack effective loop closure detection. To address these issues, we introduce NGM-SLAM, the first 3DGS based SLAM system that utilizes neural radiance field submaps for progressive scene expression, effectively integrating the strengths of neural radiance fields and 3D Gaussian Splatting. We utilize neural radiance field submaps as supervision and achieve high-quality scene expression and online loop closure adjustments through Gaussian rendering of fused submaps. Our results on multiple real-world scenes and large-scale scene datasets demonstrate that our method can achieve accurate hole filling and high-quality scene expression, supporting monocular, stereo, and RGB-D inputs, and achieving state-of-the-art scene reconstruction and tracking performance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
