Gaussian SLAM systems excel in real-time rendering and fine-grained reconstruction compared to NeRF-based systems. However, their reliance on extensive keyframes is impractical for deployment in real-world robotic systems, which typically operate under sparse-view conditions that can result in substantial holes in the map. To address these challenges, we introduce DenseSplat, the first SLAM system that effectively combines the advantages of NeRF and 3DGS. DenseSplat utilizes sparse keyframes and NeRF priors for initializing primitives that densely populate maps and seamlessly fill gaps. It also implements geometry-aware primitive sampling and pruning strategies to manage granularity and enhance rendering efficiency. Moreover, DenseSplat integrates loop closure and bundle adjustment, significantly enhancing frame-to-frame tracking accuracy. Extensive experiments on multiple large-scale datasets demonstrate that DenseSplat achieves superior performance in tracking and mapping compared to current state-of-the-art methods.

翻译：与基于NeRF的系统相比，高斯SLAM系统在实时渲染和细粒度重建方面表现出色。然而，其对大量关键帧的依赖在实际机器人系统中部署并不现实，因为这类系统通常在稀疏视角条件下运行，可能导致地图中出现大量空洞。为应对这些挑战，我们提出了DenseSplat，这是首个有效结合NeRF与3DGS优势的SLAM系统。DenseSplat利用稀疏关键帧和NeRF先验来初始化基元，使其稠密填充地图并无缝填补空隙。该系统还实现了几何感知的基元采样与剪枝策略，以管理粒度并提升渲染效率。此外，DenseSplat集成了闭环检测与光束法平差，显著提高了帧间跟踪精度。在多个大规模数据集上的广泛实验表明，与当前最先进方法相比，DenseSplat在跟踪与建图方面均实现了更优的性能。