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.
@article{arxiv.2504.16545,
title = {ToF-Splatting: Dense SLAM using Sparse Time-of-Flight Depth and Multi-Frame Integration},
author = {Andrea Conti and Matteo Poggi and Valerio Cambareri and Martin R. Oswald and Stefano Mattoccia},
journal= {arXiv preprint arXiv:2504.16545},
year = {2025}
}