Compressive Time-of-Flight 3D Imaging Using Block-Structured Sensing Matrices
This work addresses the data bandwidth bottleneck in ToF 3D imaging for applications like gaming and automotive safety, but the approach is incremental as it applies compressive sensing with block-structured matrices to a known problem.
The paper proposes a compressive Time-of-Flight 3D camera design using block-structured sensing matrices to reduce data read-out while maintaining high spatial and temporal resolution. Global TV-regularization achieved the best reconstruction quality, with PSNR improvements over other methods.
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.