Accelerated 3d image reconstruction for resource constrained systems

Andreas Aßmann*, Yun Wu, Brian Stewart, Andrew M. Wallace

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)
50 Downloads (Pure)


We demonstrate an efficient and accelerated implementation of a parallel sparse depth reconstruction framework using compressed sensing (CS) techniques. Recent work suggests that CS can be split up into smaller sub problems. This allows us to efficiently pre-compute 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. We also investigate how reduced precision is leveraged to reduce the number of logic units in field-programmable gate array (FPGA) implementations for such sparse imaging systems. We show that the amount of logic units, memory requirements and power consumption is 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.

Original languageEnglish
Title of host publication2020 28th European Signal Processing Conference (EUSIPCO)
Number of pages5
ISBN (Electronic)9789082797053
Publication statusPublished - 18 Dec 2020
Event28th European Signal Processing Conference - Amsterdam, Netherlands
Duration: 18 Jan 202122 Jan 2021

Publication series

NameEuropean Signal Processing Conference
ISSN (Electronic)2076-1465


Conference28th European Signal Processing Conference
Abbreviated titleEUSIPCO 2020
Internet address


  • Approximate Computing
  • Compressed Sensing
  • FPGA
  • LiDAR
  • Parallelization

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering


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