Online View Sampling for Estimating Depth from Light Fields
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
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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