  Depth sensing is useful in a variety of applications that range from
augmented reality to robotics. Time-of-flight (TOF) cameras are appealing
because they obtain dense depth measurements with minimal latency. However, for
many battery-powered devices, the illumination source of a TOF camera is power
hungry and can limit the battery life of the device. To address this issue, we
present an algorithm that lowers the power for depth sensing by reducing the
usage of the TOF camera and estimating depth maps using concurrently collected
images. Our technique also adaptively controls the TOF camera and enables it
when an accurate depth map cannot be estimated. To ensure that the overall
system power for depth sensing is reduced, we design our algorithm to run on a
low power embedded platform, where it outputs 640x480 depth maps at 30 frames
per second. We evaluate our approach on several RGB-D datasets, where it
produces depth maps with an overall mean relative error of 0.96% and reduces
the usage of the TOF camera by 85%. When used with commercial TOF cameras, we
estimate that our algorithm can lower the total power for depth sensing by up
to 73%.
