Scaled 360 layouts: Revisiting non-central panoramas
Bruno Berenguel-Baeta Jesus Bermudez-Cameo Jose J. Guerrero
From a non-central panorama, 3D lines can be recovered by geometric
reasoning. However, their sensitivity to noise and the complex geometric
modeling required has led these panoramas being very little investigated. In
this work we present a novel approach for 3D layout recovery of indoor
environments using single non-central panoramas. We obtain the boundaries of
the structural lines of the room from a non-central panorama using deep
learning and exploit the properties of non-central projection systems in a new
geometrical processing to recover the scaled layout. We solve the problem for
Manhattan environments, handling occlusions, and also for Atlanta environments
in an unified method. The experiments performed improve the state-of-the-art
methods for 3D layout recovery from a single panorama. Our approach is the
first work using deep learning with non-central panoramas and recovering the
scale of single panorama layouts.