Adaptive Plenoptic Sampling: Theory and Applications
Author(s)
Gilliam, Christopher
Type
Thesis
Abstract
Image-Based Rendering (IBR) is an effective technique for rendering novel views of a
scene from multi-view images. The plenoptic function enables IBR to be formulated in
terms of sampling and reconstruction. In this thesis, we combine the theoretical results
from uniform plenoptic sampling with non-uniform camera placement. The central
concept is that geometry of the scene can be modelled with a sequence of slanted planes.
The positions of the cameras are then derived from the plenoptic spectral analysis of a
slanted plane. To this end, we present novel results for the plenoptic spectral analysis
of a slanted plane and an algorithm for adaptive plenoptic sampling.
The novelty of our spectral analysis lies in the inclusion of two realistic conditions
when calculating the plenoptic spectrum: finite scene width and cameras with finite
field of view. Using these conditions, we derive an exact closed-form expression for the
plenoptic spectrum of a slanted plane with bandlimited texture. From this spectrum,
we determine an expression for the maximum spacing between adjacent cameras. Using
synthetic and real scenes, we show that this expression is a more accurate gauge of the
Nyquist sampling density than the current state-of-the-art.
Based on these results, we design an adaptive plenoptic sampling algorithm for a
scene with a smoothly varying surface and bandlimited texture. The algorithm operates
by determining the best sequence of slanted planes to model the scene given its
geometry and a limited number of cameras. Once this sequence of planes is obtained,
the algorithm then positions the cameras using our sampling analysis of a slanted plane.
Using synthetic and real scenes, we show that this algorithm outperforms uniform sampling.
Finally, we also present a novel reconstruction filter for plenoptic sampling that outperforms the state-of-the-art for both synthetic and real scenes. The filter uses
interpolators of maximum-order-minimal-support (MOMS).
scene from multi-view images. The plenoptic function enables IBR to be formulated in
terms of sampling and reconstruction. In this thesis, we combine the theoretical results
from uniform plenoptic sampling with non-uniform camera placement. The central
concept is that geometry of the scene can be modelled with a sequence of slanted planes.
The positions of the cameras are then derived from the plenoptic spectral analysis of a
slanted plane. To this end, we present novel results for the plenoptic spectral analysis
of a slanted plane and an algorithm for adaptive plenoptic sampling.
The novelty of our spectral analysis lies in the inclusion of two realistic conditions
when calculating the plenoptic spectrum: finite scene width and cameras with finite
field of view. Using these conditions, we derive an exact closed-form expression for the
plenoptic spectrum of a slanted plane with bandlimited texture. From this spectrum,
we determine an expression for the maximum spacing between adjacent cameras. Using
synthetic and real scenes, we show that this expression is a more accurate gauge of the
Nyquist sampling density than the current state-of-the-art.
Based on these results, we design an adaptive plenoptic sampling algorithm for a
scene with a smoothly varying surface and bandlimited texture. The algorithm operates
by determining the best sequence of slanted planes to model the scene given its
geometry and a limited number of cameras. Once this sequence of planes is obtained,
the algorithm then positions the cameras using our sampling analysis of a slanted plane.
Using synthetic and real scenes, we show that this algorithm outperforms uniform sampling.
Finally, we also present a novel reconstruction filter for plenoptic sampling that outperforms the state-of-the-art for both synthetic and real scenes. The filter uses
interpolators of maximum-order-minimal-support (MOMS).
Date Issued
2013
Date Awarded
2013-02
Copyright Statement
Attribution NoDerivatives 4.0 International Licence (CC BY-ND)
Advisor
Dragotti, Pier Luigi
Brookes, Mike
Publisher Department
Electrical and Electronic Engineering
Publisher Institution
Imperial College London
Qualification Level
Doctoral
Qualification Name
Doctor of Philosophy (PhD)