  Time-of-Flight (ToF) cameras require active illumination to obtain depth
information thus the power of illumination directly affects the performance of
ToF cameras. Traditional ToF imaging algorithms is very sensitive to
illumination and the depth accuracy degenerates rapidly with the power of it.
Therefore, the design of a power efficient ToF camera always creates a painful
dilemma for the illumination and the performance trade-off. In this paper, we
show that despite the weak signals in many areas under extreme short exposure
setting, these signals as a whole can be well utilized through a learning
process which directly translates the weak and noisy ToF camera raw to depth
map. This creates an opportunity to tackle the aforementioned dilemma and make
a very power efficient ToF camera possible. To enable the learning, we collect
a comprehensive dataset under a variety of scenes and photographic conditions
by a specialized ToF camera. Experiments show that our method is able to
robustly process ToF camera raw with the exposure time of one order of
magnitude shorter than that used in conventional ToF cameras. In addition to
evaluating our approach both quantitatively and qualitatively, we also discuss
its implication to designing the next generation power efficient ToF cameras.
We will make our dataset and code publicly available.

Computer Vision and Pattern Recognition