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Computer Vision and Pattern Recognition (CVPR 2023)

Polarimetric iToF: Measuring High-Fidelity Depth through Scattering Media
  Daniel S. Jeon Andreas Meuleman Seung-Hwan Baek Min H. Kim  
  We introduce a polarimetric iToF imaging method that can estimate depth robustly through scattering media. (a) A pho- tograph of the input scene without fog. (b) Ground-truth depth measure without fog. (c) Input iToF amplitude map captured with fog. (d) Depth estimated by a conventional iToF camera with fog. (e) Depth improved by na ̈ıve cross-polarization filtering. (f) Our iToF depth measurement result is fairly close to the GT depth.  

Indirect time-of-flight (iToF) imaging allows us to capture dense depth information at a low cost. However, iToF imaging often suffers from multipath interference (MPI) artifacts in the presence of scattering media, resulting in severe depth-accuracy degradation. For instance, iToF cameras cannot measure depth accurately through fog because ToF active illumination scatters back to the sensor before reaching the farther target surface. In this work, we propose a polarimetric iToF imaging method that can capture depth information robustly through scattering media. Our observations on the principle of indirect ToF imaging and polarization of light allow us to formulate a novel computational model of scattering-aware polarimetric phase measurements that enables us to correct MPI errors. We first devise a scattering-aware polarimetric iToF model that can estimate the phase of unpolarized backscattered light. We then combine the optical filtering of polarization and our computational modeling of unpolarized backscattered light via scattering analysis of phase and amplitude. This allows us to tackle the MPI problem by estimating the scattering energy through the participating media. We validate our method on an experimental setup using a customized off-the-shelf iToF camera. Our method outperforms baseline methods by a significant margin by means of our scattering model and polarimetric phase measurements.

  CVPR 2023 presentation
   author = {Daniel S. Jeon and Andreas Meuleman and Seung-Hwan Baek and Min H. Kim},
   title = {Polarimetric iToF: Measuring High-Fidelity Depth through Scattering Media},
   booktitle = {IEEE Conference on Computer Vision and 
      Pattern Recognition (CVPR)},
   month = {June},
   year = {2023}
Preprint paper:
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material #1:
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