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.

Este artículo explora los viajes en el tiempo y sus implicaciones.

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