Accelerated 3D Image Reconstruction for Resource-Constrained Systems

This paper demonstrates an efficient and accelerated implementation of a parallel sparse depth reconstruction framework using compressed sensing (CS) techniques. The authors explore how CS can be split into smaller subproblems, allowing for the pre-computation of important components of the LU decomposition and subsequent linear algebra to solve a set of linear equations found in algorithms such as the alternating direction method of multipliers (ADMM). For comparison, a fully discrete least square reconstruction method is also presented. The study investigates how reduced precision is leveraged to reduce the number of logic units in field-programmable gate array (FPGA) implementations for such sparse imaging systems. The results show that the amount of logic units, memory requirements, and power consumption are reduced significantly by over 70% with minimal impact on the quality of reconstruction. This demonstrates the feasibility of novel high-resolution, low-power, and high frame rate light detection and ranging (LiDAR) depth imagers based on sparse illumination.

Recommended citation: Aßmann, A., Wu, Y., Stewart, B., & Wallace, A. M. (2020). Accelerated 3D Image Reconstruction for Resource-Constrained Systems. In *Proceedings of the 28th European Signal Processing Conference (EUSIPCO 2020)* (pp. 565–569). IEEE. https://doi.org/10.23919/Eusipco47968.2020.9287749
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