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.

3D floor plan recovery from overlapping spherical images

Ganovelli F;Scopigno R;
2018

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.
2018
Istituto di Scienza e Tecnologie dell'Informazione "Alessandro Faedo" - ISTI
Indoor reconstruction
Spherical panoramic cameras
360 degrees photography
multiroom environments
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/351430
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