用神经辐射先验让高斯点云SLAM在稀疏视角下也能密集建图
DenseSplat: Densifying Gaussian Splatting SLAM with Neural Radiance Prior
- 用稀疏关键帧结合NeRF先验初始化稠密点云，填补地图空洞
- 几何感知采样与剪枝策略提升建图精度和渲染效率
- 支持回环检测与捆绑调整，适合真实机器人场景
与基于NeRF的系统相比，高斯SLAM系统在实时渲染和精细重建方面表现更优。然而，其依赖大量关键帧的特性在真实机器人系统中难以实现，这类系统通常面临稀疏视角，导致地图存在显著空洞。为此，我们提出DenseSplat，首个有效融合NeRF与3DGS优势的SLAM系统。DenseSplat利用稀疏关键帧和NeRF先验初始化稠密点云，以无缝填充地图空隙。同时引入几何感知的点云采样与剪枝策略，优化粒度并提升渲染效率。此外，系统集成回环检测与捆绑调整，显著提升帧间跟踪精度。在多个大规模数据集上的实验表明，DenseSplat在跟踪与建图性能上均优于当前最先进方法。
原文摘要 · Abstract (English)
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.
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