Compressive time-of-flight 3D imaging using block-structured sensing matrices. (5th March 2019)

• Record Type: Journal Article
• Authors: Antholzer, Stephan; Wolf, Christoph; Sandbichler, Michael; Dielacher, Markus; Haltmeier, Markus
• Is Part Of: Inverse problems. Volume 35:Number 4(2019)
• Publication Date: 2019-03-05
• Subjects: time-of-flight imaging -- image reconstruction -- 3D imaging -- sparse recovery -- compressed sensing -- total variation; Inverse problems (Differential equations) -- Periodicals; 515.357
• DOI: 10.1088/1361-6420/aafce3
• Languages: English
• ISSNs: 0266-5611

Abstract

Abstract: 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 one 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 one to reduce the amount of data while keeping high spatial and temporal resolution. We propose the use of efficient reconstruction algorithms based on -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 -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.