ToF-Splatting：使用稀疏飞行时间深度和多帧集成的稠密SLAM
ToF-Splatting: Dense SLAM using Sparse Time-of-Flight Depth and Multi-Frame Integration
AI总结：
针对稀疏ToF深度在SLAM中难以使用的问题，提出首个基于3D高斯泼溅的SLAM流程ToF-Splatting，通过多帧集成模块融合稀疏ToF、单目颜色和多视图几何生成稠密深度图，在合成和真实数据集上达到最先进性能。
摘要：
飞行时间（ToF）传感器在相对较低的功耗预算下提供高效的主动深度感知；在这类设计中，只有来自低分辨率传感器的非常稀疏的测量值被认为能满足移动和AR/VR设备日益严格的功耗限制。然而，这种极端的稀疏程度限制了ToF深度在SLAM中的无缝使用。在这项工作中，我们提出了ToF-Splatting，这是第一个基于3D高斯泼溅的SLAM流程，专门用于有效利用非常稀疏的ToF输入数据。我们的方法通过引入一个多帧集成模块来改进现有技术水平，该模块通过合并来自极稀疏ToF深度、单目颜色和多视图几何的线索来生成稠密深度图。在合成和真实稀疏ToF数据集上的大量实验证明了我们方法的可行性，因为它在参考数据集上实现了最先进的跟踪和建图性能。
英文摘要：
Time-of-Flight (ToF) sensors provide efficient active depth sensing at relatively low power budgets; among such designs, only very sparse measurements from low-resolution sensors are considered to meet the increasingly limited power constraints of mobile and AR/VR devices. However, such extreme sparsity levels limit the seamless usage of ToF depth in SLAM. In this work, we propose ToF-Splatting, the first 3D Gaussian Splatting-based SLAM pipeline tailored for using effectively very sparse ToF input data. Our approach improves upon the state of the art by introducing a multi-frame integration module, which produces dense depth maps by merging cues from extremely sparse ToF depth, monocular color, and multi-view geometry. Extensive experiments on both synthetic and real sparse ToF datasets demonstrate the viability of our approach, as it achieves state-of-the-art tracking and mapping performances on reference datasets.
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