Online View Sampling for Estimating Depth from Light Fields

ICIP 2015

Changil Kim1,2, Kartic Subr1, Kenny Mitchell1, Alexander Sorkine-Hornung1, Markus Gross1,2

1Disney Research Zurich, 2ETH Zurich

Teaser image thumbnail

Resulting depth maps computed after several iterations with k = 2. Our sampling strategy (top row) is compared against the regular sampling (bottom row). The error plots (last column; the lower the better) show the faster convergence of ours towards lower errors.


Geometric information such as depth obtained from light fields finds more applications recently. Where and how to sample images to populate a light field is an important problem to maximize the usability of information gathered for depth reconstruction. We propose a simple analysis model for view sampling and an adaptive, online sampling algorithm tailored to light field depth reconstruction. Our model is based on the trade-off between visibility and depth resolvability for varying sampling locations, and seeks the optimal locations that best balance the two conflicting criteria.



  author    = {Changil Kim and Kartic Subr and Kenny Mitchell and Alexander Sorkine-Hornung and Markus Gross},
  title     = {Online View Sampling for Estimating Depth from Light Fields},
  booktitle = {Proceedings of IEEE International Conference on Image Processing (ICIP)},
  year      = {2015},
  pages     = {1155--1159},