Dense simultaneous localization and mapping (SLAM) is pivotal for embodied
scene understanding. Recent work has shown that 3D Gaussians enable
high-quality reconstruction and real-time rendering of scenes using multiple
posed cameras. In this light, we show for the first time that representing a
scene by 3D Gaussians can enable dense SLAM using a single unposed monocular
RGB-D camera. Our method, SplaTAM, addresses the limitations of prior radiance
field-based representations, including fast rendering and optimization, the
ability to determine if areas have been previously mapped, and structured map
expansion by adding more Gaussians. We employ an online tracking and mapping
pipeline while tailoring it to specifically use an underlying Gaussian
representation and silhouette-guided optimization via differentiable rendering.
Extensive experiments show that SplaTAM achieves up to 2X state-of-the-art
performance in camera pose estimation, map construction, and novel-view
synthesis, demonstrating its superiority over existing approaches, while
allowing real-time rendering of a high-resolution dense 3D map.

用单个未对齐的单目 RGB-D 相机，通过使用 3D 高斯模型表示场景，我们首次展示了实现稠密的同时定位和映射 (SLAM)。我们的方法 SplaTAM 解决了基于辐射场的先前表示的限制，包括快速渲染和优化、确定区域是否已被映射和通过添加更多高斯模型进行结构化地图扩展。大量实验证明 SplaTAM 在相机定位、地图构建和新视图合成方面的性能达到了现有方法的 2 倍，并表现出优于现有方法的优越性，同时实现了高分辨率稠密 3D 地图的实时渲染。