Title:
Rao-Blackwellized Importance Sampling of Camera Parameters from Simple User Input with Visibility Preprocessing in Line Space

dc.contributor.author Quennesson, Kevin
dc.contributor.author Dellaert, Frank
dc.contributor.corporatename Georgia Institute of Technology. Center for Robotics and Intelligent Machines
dc.contributor.corporatename Georgia Institute of Technology. College of Computing
dc.date.accessioned 2011-04-15T14:51:46Z
dc.date.available 2011-04-15T14:51:46Z
dc.date.issued 2006-06
dc.description.abstract Users know what they see before where they are: it is more natural to talk about high level visibility information ("I see such object") than about one's location or orientation. In this paper we introduce a method to find in 3D worlds a density of viewpoints of camera locations from high level visibility constraints on objects in this world. Our method is based on Rao-Blackwellized importance sampling. For efficiency purposes, the proposal distribution used for sampling is extracted from a visibility preprocessing technique adapted from computer graphics. We apply the method for finding in a 3D city model of Atlanta the virtual locations of real-world cameras and viewpoints.
dc.identifier.citation Quennesson, K. and Dellaert, F. (2006). Rao-Blackwellized Importance Sampling of Camera Parameters from Simple User Input with Visibility Preprocessing in Line Space. Third International Symposium 3D Data Processing, Visualization, and Transmission, June 2006. en_US
dc.identifier.uri http://hdl.handle.net/1853/38542
dc.language.iso en_US en_US
dc.publisher Georgia Institute of Technology en_US
dc.title Rao-Blackwellized Importance Sampling of Camera Parameters from Simple User Input with Visibility Preprocessing in Line Space en_US
dc.type Text
dc.type.genre Poster
dspace.entity.type Publication
local.contributor.author Dellaert, Frank
local.contributor.corporatename Institute for Robotics and Intelligent Machines (IRIM)
local.contributor.corporatename College of Computing
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relation.isOrgUnitOfPublication c8892b3c-8db6-4b7b-a33a-1b67f7db2021
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