Atlanta scaled layouts from non-central panoramas

Abstract

In this work we present a novel approach for 3D layout recovery of indoor environments using a non-central acquisition system. From a single non-central panorama, full and scaled 3D lines can be independently recovered by geometry reasoning without additional nor scale assumptions. However, their sensitivity to noise and complex geometric modeling has led these panoramas and required algorithms being little investigated. Our new pipeline aims to extract the boundaries of the structural lines of an indoor environment with a neural network and exploit the properties of non-central projection systems in a new geometrical processing to recover scaled 3D layouts. The results of our experiments show that we improve state-of-the-art methods for layout recovery and line extraction in non-central projection systems. We completely solve the problem both in Manhattan and Atlanta environments, handling occlusions and retrieving the metric scale of the room without extra measurements. As far as the authors’ knowledge goes, our approach is the first work using deep learning on non-central panoramas and recovering scaled layouts from single panoramas.