Initial applications of 3D Gaussian Splatting (3DGS) in Visual Simultaneous Localization and Mapping (VSLAM) demonstrate the generation of high-quality volumetric reconstructions from monocular video streams. However, despite these promising advancements, current 3DGS integrations have reduced tracking performance and lower operating speeds compared to traditional VSLAM. To address these issues, we propose integrating 3DGS with Direct Sparse Odometry, a monocular photometric SLAM system. We have done preliminary experiments showing that using Direct Sparse Odometry point cloud outputs, as opposed to standard structure-from-motion methods, significantly shortens the training time needed to achieve high-quality renders. Reducing 3DGS training time enables the development of 3DGS-integrated SLAM systems that operate in real-time on mobile hardware. These promising initial findings suggest further exploration is warranted in combining traditional VSLAM systems with 3DGS.

翻译：三维高斯溅射（3DGS）在视觉同步定位与建图（VSLAM）中的初步应用展示了从单目视频流生成高质量体素重建的能力。然而，尽管取得了这些令人鼓舞的进展，当前3DGS集成方案的跟踪性能与运行速度仍低于传统VSLAM系统。为解决这些问题，我们提出将3DGS与直接稀疏里程计（一种单目光度SLAM系统）相结合。初步实验表明，相较于标准运动恢复结构方法，采用直接稀疏里程计点云输出可显著缩短实现高质量渲染所需的训练时间。3DGS训练时间的缩减使得开发能在移动硬件上实时运行的3DGS集成SLAM系统成为可能。这些积极的初步发现表明，将传统VSLAM系统与3DGS相结合值得进一步深入探索。