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3D floor plan recovery from overlapping spherical images

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

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 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.

Reference and download information

Giovanni Pintore, Fabio Ganovelli, Ruggero Pintus, Roberto Scopigno, and Enrico Gobbetti. 3D floor plan recovery from overlapping spherical images. Computational Visual Media, 2018. To appear.

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Bibtex citation record

@Article{Pintore:2018:3FP,
    author = {Giovanni Pintore and Fabio Ganovelli and Ruggero Pintus and Roberto Scopigno and Enrico Gobbetti},
    title = {{3D} floor plan recovery from overlapping spherical images},
    journal = {Computational Visual Media},
    publisher = {Springer Verlag},
    address = {New York, NY, USA},
    year = {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. },
    note = {To appear},
    url = {http://vic.crs4.it/vic/cgi-bin/bib-page.cgi?id='Pintore:2018:3FP'},
}