Online View Sampling for Estimating Depth from Light Fields

 

We propose a simple analysis model for view sampling and an adaptive, online sampling algorithm tailored to light field depth reconstruction.

September 27, 2015
International Conference on Image Processing (ICIP) 2015

 

Authors

Changil Kim (Disney Research/ETH Joint PhD)

Kartic Subr (Disney Research)

Kenny Mitchell (Disney Research)

Alexander Sorkine-Hornung (Disney Research) 

Markus Gross (Disney Research/ETH Zurich)

Online View Sampling for Estimating Depth from Light Fields

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Abstract

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.

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