A Bayesian Approach for Selective Image-Based Rendering using Superpixels
Abstract
Image-Based Rendering (IBR) algorithms generate high
quality photo-realistic imagery without the burden of detailed
modeling and expensive realistic rendering. Recent
methods have different strengths and weaknesses, depending
on 3D reconstruction quality and scene content. Each
algorithm operates with a set of hypotheses about the scene
and the novel views, resulting in different quality/speed
trade-offs in different image regions. We present a principled
approach to select the algorithm with the best quality/speed
trade-off in each region. To do this, we propose
a Bayesian approach, modeling the rendering quality, the
rendering process and the validity of the assumptions of
each algorithm. We then choose the algorithm to use with
Maximum a Posteriori estimation. We demonstrate the utility
of our approach on recent IBR algorithms which use
oversegmentation and are based on planar reprojection and
shape-preserving warps respectively. Our algorithm selects
the best rendering algorithm for each superpixel in a
preprocessing step; at runtime our selective IBR uses this
choice to achieve significant speedup at equivalent or better
quality compared to previous algorithms.
Domains
Graphics [cs.GR]
Origin : Files produced by the author(s)
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