GS-SLAM：基于3D Gaussian Splatting的稠密视觉SLAM
GS-SLAM: Dense Visual SLAM with 3D Gaussian Splatting
AI总结：
提出GS-SLAM，将3D Gaussian表示与可微splatting渲染引入稠密视觉SLAM，并通过自适应高斯扩展和由粗到细位姿跟踪实现高效、鲁棒且具有竞争力的实时性能。
摘要：
本文提出GS-SLAM，首次在同步定位与地图构建（SLAM）系统中使用3D Gaussian表示，从而在效率与精度之间实现更好的平衡。与近期采用神经隐式表示的SLAM方法相比，该方法使用实时可微splatting渲染管线，显著加快地图优化和RGB-D渲染。具体而言，作者提出一种自适应扩展策略，通过添加新的3D Gaussian或删除含噪3D Gaussian，高效重建新观测到的场景几何，并改善此前观测区域的建图效果。该策略对于将3D Gaussian表示扩展到整个场景重建至关重要，而不是像现有方法那样仅合成静态对象。此外，在位姿跟踪过程中，作者设计了有效的由粗到细技术，用于选择可靠的3D Gaussian表示来优化相机位姿，从而减少运行时间并获得鲁棒估计。在Replica和TUM-RGBD数据集上，该方法相比现有最先进的实时方法取得了具有竞争力的性能。项目页面：https://gs-slam.github.io/。
英文摘要：
In this paper, we introduce \textbf{GS-SLAM} that first utilizes 3D Gaussian representation in the Simultaneous Localization and Mapping (SLAM) system. It facilitates a better balance between efficiency and accuracy. Compared to recent SLAM methods employing neural implicit representations, our method utilizes a real-time differentiable splatting rendering pipeline that offers significant speedup to map optimization and RGB-D rendering. Specifically, we propose an adaptive expansion strategy that adds new or deletes noisy 3D Gaussians in order to efficiently reconstruct new observed scene geometry and improve the mapping of previously observed areas. This strategy is essential to extend 3D Gaussian representation to reconstruct the whole scene rather than synthesize a static object in existing methods. Moreover, in the pose tracking process, an effective coarse-to-fine technique is designed to select reliable 3D Gaussian representations to optimize camera pose, resulting in runtime reduction and robust estimation. Our method achieves competitive performance compared with existing state-of-the-art real-time methods on the Replica, TUM-RGBD datasets. Project page: https://gs-slam.github.io/.
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。
