Online view sampling for estimating depth from light fields
Chang-Il Kim, Kartic Subr, Kenny Mitchell, Alexander Sorkine‐Hornung, Markus H. Groß · 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.