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Title: Adaptive plenoptic sampling : theory and applications
Author: Gilliam, Christopher
Awarding Body: Imperial College London
Current Institution: Imperial College London
Date of Award: 2013
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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).
Supervisor: Dragotti, Pier Luigi ; Brookes, Mike Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available