Online View Sampling for Estimating Depth from Light Fields

Authors

Changil Kim (Disney Research Zurich)
Kartic Subr (Disney Research Zurich)
Kenny Mitchell (Disney Research Los Angeles)
Alexander Sorkine-Hornung (Disney Research Zurich)
Markus Gross (Disney Research Zurich)

International Conference on Image Processing (ICIP) 2015

September 27, 2015

Online View Sampling for Estimating Depth from Light Fields-Image

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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