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

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.