  Spatially and temporally highly resolved depth information enables numerous
applications including human-machine interaction in gaming or safety functions
in the automotive industry. In this paper, we address this issue using
Time-of-flight (ToF) 3D cameras which are compact devices providing highly
resolved depth information. Practical restrictions often require to reduce the
amount of data to be read-out and transmitted. Using standard ToF cameras, this
can only be achieved by lowering the spatial or temporal resolution. To
overcome such a limitation, we propose a compressive ToF camera design using
block-structured sensing matrices that allows to reduce the amount of data
while keeping high spatial and temporal resolution. We propose the use of
efficient reconstruction algorithms based on l^1-minimization and
TV-regularization. The reconstruction methods are applied to data captured by a
real ToF camera system and evaluated in terms of reconstruction quality and
computational effort. For both, l^1-minimization and TV-regularization, we use
a local as well as a global reconstruction strategy. For all considered
instances, global TV-regularization turns out to clearly perform best in terms
of evaluation metrics including the PSNR.
