Compressive Time-of-Flight 3D Imaging Using Block-Structured Sensing Matrices

TL;DR

This paper introduces a compressive ToF 3D imaging approach using block-structured sensing matrices, enabling high-resolution depth data acquisition with reduced data read-out, and demonstrates effective reconstruction algorithms with superior results.

Contribution

It presents a novel compressive sensing design for ToF cameras with block-structured matrices and evaluates reconstruction algorithms for improved depth imaging.

Findings

Global TV-regularization outperforms other methods in reconstruction quality.

The proposed approach reduces data requirements while maintaining high spatial and temporal resolution.

Reconstruction algorithms are effective on real ToF camera data.

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 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.