A Framework for Compressive Time-of-Flight 3D Sensing

Spatially and temporally highly resolved depth information enables numerous applications including human-machine interaction in gaming industry 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 that allows to reduce the amount of data while keeping high spatial and temporal resolution. This uses the theory of compressive sensing and sparse recovery. We propose efficient block-wise reconstruction algorithms based on `1-minimization. We apply the developed reconstruction methods to data captured by a real ToF camera system and evaluate them in terms of reconstruction quality and computational effort.