Multi-view stereo is the process of recreating three-dimensional data from a set of two or more images of a scene. The ability to acquire 3D data from 2D images is a core concept in computer vision with wide-ranging applications throughout areas such as 3D printing, robotics, recognition, navigation and a vast number of other fields. While 3D reconstruction has been increasingly well studied over the past decades, it is only with the recent evolution of CPU and GPU technologies that practical implementations, able to accurately, robustly and efficiently capture 3D data of photographed objects have begun to emerge. Whilst current research has been shown to perform well under specific circumstances and for a subset of objects, there are still many practical and implementary issues that remain an open problem for these techniques. Most notably, the ability to robustly reconstruct objects from sparse image sets or objects with low texture. Alongside a review of algorithms within the multi-view field, the work proposed in this thesis outlines a massively parallel patch based multi-view stereo pipeline for static scene recovery. By utilising advances in GPU technology, a particle swarm algorithm implemented on the GPU forms the basis for improving the density of patch-based methods. The novelty of such an approach removes the reliance on feature matching and gradient descent to better account for the optimisation of patches within textureless regions, for which current methods struggle. An enhancement to the photo-consistency matching metric, which is used to evaluate the optimisation of each patch, is then defined. Specifically targeting the shortcomings of the photo-consistency metric when used inside a particle swarm optimisation, increasing its effectiveness over textureless areas. Finally, a multi-resolution reconstruction system based on a wavelet framework is presented to further improve upon the robustness of reconstruction over low textured regions.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:689831 |
| Date | January 2016 |
| Creators | Haines, Benjamin A. |
| Publisher | University of Nottingham |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://eprints.nottingham.ac.uk/32746/ |
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