  Light-weight time-of-flight (ToF) depth sensors are compact and
cost-efficient, and thus widely used on mobile devices for tasks such as
autofocus and obstacle detection. However, due to the sparse and noisy depth
measurements, these sensors have rarely been considered for dense geometry
reconstruction. In this work, we present the first dense SLAM system with a
monocular camera and a light-weight ToF sensor. Specifically, we propose a
multi-modal implicit scene representation that supports rendering both the
signals from the RGB camera and light-weight ToF sensor which drives the
optimization by comparing with the raw sensor inputs. Moreover, in order to
guarantee successful pose tracking and reconstruction, we exploit a predicted
depth as an intermediate supervision and develop a coarse-to-fine optimization
strategy for efficient learning of the implicit representation. At last, the
temporal information is explicitly exploited to deal with the noisy signals
from light-weight ToF sensors to improve the accuracy and robustness of the
system. Experiments demonstrate that our system well exploits the signals of
light-weight ToF sensors and achieves competitive results both on camera
tracking and dense scene reconstruction. Project page:
\url{https://zju3dv.github.io/tof_slam/}.
