Abstract
abstract
Abstract We present a novel approach to automatically recover, from a small set of partially overlapping spherical images, an indoor structure representation in terms of a 3D floor plan registered with a set of 3D environment maps. % We introduce several improvements over previous approaches based on color/spatial reasoning exploiting \emph{Manhattan World} priors. In particular, we introduce a new method for geometric context extraction based on a 3D facets representation, which combines color distribution analysis of individual images with sparse multi-view clues. Moreover, we introduce an efficient method to combine the facets from different points of view in a single consistent model, considering the reliability of the facets contribution. The resulting capture and reconstruction pipeline automatically generates 3D multi-room environments where most of the other previous approaches fail, such as in presence of hidden corners and large clutter, even without involving additional dense 3D data or tools. % We demonstrate the effectiveness and performance of our approach on different real-world indoor scenes. Our test data will be released to allow for further studies and comparisons.
