000118033 001__ 118033 000118033 005__ 20240319081002.0 000118033 0247_ $$2doi$$a10.1016/j.patcog.2022.108740 000118033 0248_ $$2sideral$$a128708 000118033 037__ $$aART-2022-128708 000118033 041__ $$aeng 000118033 100__ $$0(orcid)0000-0003-2674-4844$$aBerenguel-Baeta, B.$$uUniversidad de Zaragoza 000118033 245__ $$aAtlanta scaled layouts from non-central panoramas 000118033 260__ $$c2022 000118033 5060_ $$aAccess copy available to the general public$$fUnrestricted 000118033 5203_ $$aIn 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. 000118033 536__ $$9info:eu-repo/grantAgreement/ES/AEI-FEDER/RTI2018-096903-B-I00 000118033 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc-nd$$uhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/ 000118033 590__ $$a8.0$$b2022 000118033 592__ $$a2.085$$b2022 000118033 591__ $$aENGINEERING, ELECTRICAL & ELECTRONIC$$b30 / 274 = 0.109$$c2022$$dQ1$$eT1 000118033 593__ $$aArtificial Intelligence$$c2022$$dQ1 000118033 591__ $$aCOMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE$$b25 / 145 = 0.172$$c2022$$dQ1$$eT1 000118033 593__ $$aSoftware$$c2022$$dQ1 000118033 593__ $$aSignal Processing$$c2022$$dQ1 000118033 593__ $$aComputer Vision and Pattern Recognition$$c2022$$dQ1 000118033 594__ $$a13.9$$b2022 000118033 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion 000118033 700__ $$0(orcid)0000-0002-8479-1748$$aBermudez-Cameo, J.$$uUniversidad de Zaragoza 000118033 700__ $$0(orcid)0000-0001-5209-2267$$aGuerrero, J.J.$$uUniversidad de Zaragoza 000118033 7102_ $$15007$$2520$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Ingen.Sistemas y Automát. 000118033 773__ $$g129 (2022), 108740 [13 pp.]$$pPattern recogn.$$tPattern Recognition$$x0031-3203 000118033 8564_ $$s3413475$$uhttps://zaguan.unizar.es/record/118033/files/texto_completo.pdf$$yVersión publicada 000118033 8564_ $$s2721398$$uhttps://zaguan.unizar.es/record/118033/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada 000118033 909CO $$ooai:zaguan.unizar.es:118033$$particulos$$pdriver 000118033 951__ $$a2024-03-18-14:13:36 000118033 980__ $$aARTICLE