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Recovering 3D indoor floor plans by exploiting low-cost spherical photography

Giovanni Pintore, Fabio Ganovelli, Ruggero Pintus, Roberto Scopigno, and Enrico Gobbetti

October 2018

Abstract

We present a novel approach to automatically recover, from a small set of partially overlapping panoramic images, an indoor structure representation in terms of a 3D floor plan registered with a set of 3D environment maps. Our improvements over previous approaches include 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, as well as an efficient method to combine the facets from different point-of-view in the same world space, 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, large clutter and sloped ceilings, even without involving additional dense 3D data or tools. We demonstrate the effectiveness and performance of our approach on different real-world indoor scenes.

Reference and download information

Giovanni Pintore, Fabio Ganovelli, Ruggero Pintus, Roberto Scopigno, and Enrico Gobbetti. Recovering 3D indoor floor plans by exploiting low-cost spherical photography. In Pacific Graphics 2018 Short Papers, October 2018. To appear.

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Bibtex citation record

@InProceedings{Pintore:2018:R3I,
    author = {Giovanni Pintore and Fabio Ganovelli and Ruggero Pintus and Roberto Scopigno and Enrico Gobbetti},
    title = {Recovering 3D indoor floor plans by exploiting low-cost spherical photography},
    booktitle = {Pacific Graphics 2018 Short Papers},
    month = {October},
    year = {2018},
    abstract = { We present a novel approach to automatically recover, from a small set of partially overlapping panoramic images, an indoor structure representation in terms of a 3D floor plan registered with a set of 3D environment maps. Our improvements over previous approaches include 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, as well as an efficient method to combine the facets from different point-of-view in the same world space, 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, large clutter and sloped ceilings, even without involving additional dense 3D data or tools. We demonstrate the effectiveness and performance of our approach on different real-world indoor scenes. },
    note = {To appear},
    url = {http://vic.crs4.it/vic/cgi-bin/bib-page.cgi?id='Pintore:2018:R3I'},
}