  Integrating an RGB camera into a ToF imaging system has become a significant
technique for perceiving the real world. The RGB guided ToF imaging system is
crucial to several applications, including face anti-spoofing, saliency
detection, and trajectory prediction. Depending on the distance of the working
range, the implementation schemes of the RGB guided ToF imaging systems are
different. Specifically, ToF sensors with a uniform field of illumination,
which can output dense depth but have low resolution, are typically used for
close-range measurements. In contrast, LiDARs, which emit laser pulses and can
only capture sparse depth, are usually employed for long-range detection. In
the two cases, depth quality improvement for RGB guided ToF imaging corresponds
to two sub-tasks: guided depth super-resolution and guided depth completion. In
light of the recent significant boost to the field provided by deep learning,
this paper comprehensively reviews the works related to RGB guided ToF imaging,
including network structures, learning strategies, evaluation metrics,
benchmark datasets, and objective functions. Besides, we present quantitative
comparisons of state-of-the-art methods on widely used benchmark datasets.
Finally, we discuss future trends and the challenges in real applications for
further research.
