We propose RTG-SLAM, a real-time 3D reconstruction system with an RGBD camera for large-scale environments using Gaussian splatting. RTG-SLAM features a compact Gaussian representation and a highly efficient on-the-fly Gaussian optimization scheme. We force each Gaussian to be either opaque or nearly transparent, with the opaque ones fitting the surface and dominant colors, and transparent ones fitting residual colors. By rendering depth in a different way from color rendering, we let a single opaque Gaussian well fit a local surface region without the need of multiple overlapping Gaussians, hence largely reducing the memory and computation cost. For on-the-fly Gaussian optimization, we explicitly add Gaussians for three types of pixels per frame: newly observed, with large color errors and with large depth errors. We also categorize all Gaussians into stable and unstable ones, where the stable Gaussians are expected to well fit previously observed RGBD images and otherwise unstable. We only optimize the unstable Gaussians and only render the pixels occupied by unstable Gaussians. In this way, both the number of Gaussians to be optimized and pixels to be rendered are largely reduced, and the optimization can be done in real time. We show real-time reconstructions of a variety of real large scenes. Compared with the state-of-the-art NeRF-based RGBD SLAM, our system achieves comparable high-quality reconstruction but with around twice the speed and half the memory cost, and shows superior performance in the realism of novel view synthesis and camera tracking accuracy.

通过使用高斯喷洒技术，我们提出了RTG-SLAM，一个用于大规模环境的实时3D重建系统。与现有的基于NeRF的RGBD SLAM相比，我们的系统在速度和内存成本方面取得了可比的高质量重建，并且在新视角合成的逼真度和相机跟踪准确性上表现出更好的性能。

RTG-SLAM：使用高斯喷洒实现大规模实时3D重建