A Calibration Scheme for Non-Line-of-Sight Imaging Setups

arxiv:1912.09923 [eess.IV], Dez. 2019
 

Abstract

The recent years have given rise to a large number of techniques for “looking around corners”, i.e., for reconstructing occluded objects from time-resolved measurements of indirect light reflections off a wall. While the direct view of cameras is routinely calibrated in computer vision applications, the calibration of non-line-of-sight setups has so far relied on manual measurement of the most important dimensions (device positions, wall position and orientation, etc.). In this paper, we propose a semi-automatic method for calibrating such systems that relies on mirrors as known targets. A roughly determined initialization is refined in order to optimize a spatio-temporal consistency. Our system is general enough to be applicable to a variety of sensing scenarios ranging from single sources/detectors via scanning arrangements to large-scale arrays. It is robust towards bad initialization and the achieved accuracy is proportional to the depth resolution of the camera system. We demonstrate this capability with a real-world setup and despite a large number of dead pixels and very low temporal resolution achieve a result that outperforms a manual calibration.

Bilder

Paper herunterladen

Paper herunterladen

Bibtex

@UNPUBLISHED{Klein2019,
    author = {Klein, Jonathan and Laurenzis, Martin and Hullin, Matthias B. and Iseringhausen, Julian},
     title = {A Calibration Scheme for Non-Line-of-Sight Imaging Setups},
   journal = {arXiv},
      year = {2019},
     month = dec,
      note = {arxiv:1912.09923 [eess.IV]},
  abstract = {The recent years have given rise to a large number of techniques for “looking around corners”,
              i.e., for reconstructing occluded objects from time-resolved measurements of indirect light
              reflections off a wall. While the direct view of cameras is routinely calibrated in computer vision
              applications, the calibration of non-line-of-sight setups has so far relied on manual measurement of
              the most important dimensions (device positions, wall position and orientation, etc.). In this
              paper, we propose a semi-automatic method for calibrating such systems that relies on mirrors as
              known targets. A roughly determined initialization is refined in order to optimize a spatio-temporal
              consistency. Our system is general enough to be applicable to a variety of sensing scenarios ranging
              from single sources/detectors via scanning arrangements to large-scale arrays. It is robust towards
              bad initialization and the achieved accuracy is proportional to the depth resolution of the camera
              system. We demonstrate this capability with a real-world setup and despite a large number of dead
              pixels and very low temporal resolution achieve a result that outperforms a manual calibration.},
       url = {https://arxiv.org/abs/1912.09923}
}