用极稀疏的深度数据实现高精度三维定位与建图
ToF-Splatting: Dense SLAM using Sparse Time-of-Flight Depth and Multi-Frame Integration
- 基于3D高斯点云，融合多帧彩色图像和几何信息补全深度
- 在极稀疏深度输入下仍达到顶尖追踪与建图效果
- 适合低功耗移动设备与AR/VR场景的实时三维感知
飞行时间（ToF）传感器可在低功耗下实现高效的主动测距；然而，为满足移动和AR/VR设备日益严苛的功耗限制，当前设计通常仅采用低分辨率、极稀疏的深度测量。这种极端稀疏性严重制约了ToF深度在SLAM中的无缝应用。本文提出ToF-Splatting，首个面向极稀疏ToF输入的3D高斯点云拼贴式SLAM系统。通过引入多帧融合模块，结合极稀疏的ToF深度、单目彩色图像及多视角几何信息，生成稠密深度图。在合成与真实稀疏ToF数据集上的大量实验表明，该方法在参考数据集上实现了最先进的跟踪与建图性能。
原文摘要 · Abstract (English)
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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