118033 20240319081002.0 doi 10.1016/j.patcog.2022.108740 sideral 128708 ART-2022-128708 eng Berenguel-Baeta, B. Universidad de Zaragoza (orcid)0000-0003-2674-4844 Atlanta scaled layouts from non-central panoramas 2022 Access copy available to the general public Unrestricted 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. info:eu-repo/grantAgreement/ES/AEI-FEDER/RTI2018-096903-B-I00 info:eu-repo/semantics/openAccess by-nc-nd http://creativecommons.org/licenses/by-nc-nd/3.0/es/ 8.0 2022 ENGINEERING, ELECTRICAL & ELECTRONIC 30 / 274 = 0.109 2022 Q1 T1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE 25 / 145 = 0.172 2022 Q1 T1 2.085 2022 Artificial Intelligence 2022 Q1 Software 2022 Q1 Signal Processing 2022 Q1 Computer Vision and Pattern Recognition 2022 Q1 13.9 2022 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Bermudez-Cameo, J. Universidad de Zaragoza (orcid)0000-0002-8479-1748 Guerrero, J.J. Universidad de Zaragoza (orcid)0000-0001-5209-2267 5007 520 Universidad de Zaragoza Dpto. Informát.Ingenie.Sistms. Área Ingen.Sistemas y Automát. 129 (2022), 108740 [13 pp.] Pattern recogn. Pattern Recognition 0031-3203 3413475 http://zaguan.unizar.es/record/118033/files/texto_completo.pdf Versión publicada 2721398 http://zaguan.unizar.es/record/118033/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:118033 articulos driver 2024-03-18-14:13:36 ARTICLE