Deep Disentangled Representations for Volumetric Reconstruction
dc.contributor.advisor | Gerven, M.A.J. van | |
dc.contributor.advisor | Kohli, P. | |
dc.contributor.author | Grant, E.N. | |
dc.contributor.other | University of Cambridge | en_US |
dc.date.issued | 2016-08-31 | |
dc.description.abstract | We introduce a convolutional neural network for inferring a compact disentangled graphical description of objects from 2D images that can be used for volumetric reconstruction. The network comprises an encoder and a twin-tailed decoder. The encoder generates a disentangled graphics code. The first decoder generates a volume, and the second decoder reconstructs the input image using a novel training regime that allows the graphics code to learn a separate representation of the 3D object and a description of its lighting and pose conditions. We demonstrate this method by generating volumes and disentangled graphical descriptions from images and videos of faces and chairs. | en_US |
dc.identifier.uri | http://theses.ubn.ru.nl/handle/123456789/4396 | |
dc.language.iso | en | en_US |
dc.thesis.faculty | Faculteit der Sociale Wetenschappen | en_US |
dc.thesis.specialisation | Master Artificial Intelligence | en_US |
dc.thesis.studyprogramme | Artificial Intelligence | en_US |
dc.thesis.type | Master | en_US |
dc.title | Deep Disentangled Representations for Volumetric Reconstruction | en_US |
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