149147 20251017144550.0 doi 10.1016/j.imavis.2019.04.008 sideral 113202 ART-2019-113202 eng Perez-Yus, Alejandro Universidad de Zaragoza (orcid)0000-0002-8949-2632 Scaled layout recovery with wide field of view RGB-D 2019 Access copy available to the general public Unrestricted In this work, we propose a method that integrates depth and fisheye cameras to obtain a wide 3D scene reconstruction with scale in one single shot. The motivation of such integration is to overcome the narrow field of view in consumer RGB-D cameras and lack of depth and scale information in fisheye cameras. The hybrid camera system we use is easy to build and calibrate, and currently consumer devices with similar configuration are already available in the market. With this system, we have a portion of the scene with shared field of view that provides simultaneously color and depth. In the rest of the color image we estimate the depth by recovering the structural information of the scene. Our method finds and ranks corners in the scene combining the extraction of lines in the color image and the depth information. These corners are used to generate plausible layout hypotheses, which have real-world scale due to the usage of depth. The wide angle camera captures more information from the environment (e.g. the ceiling), which helps to overcome severe occlusions. After an automatic evaluation of the hypotheses, we obtain a scaled 3D model expanding the original depth information with the wide scene reconstruction. We show in our experiments with real images from both home-made and commercial systems that our method achieves high success ratio in different scenarios and that our hybrid camera system outperforms the single color camera set-up while additionally providing scale in one single shot. (C) 2019 Elsevier B.V. All rights reserved. info:eu-repo/grantAgreement/ES/MINECO/BES-2013-065834 info:eu-repo/grantAgreement/ES/MINECO/DPI2014-61792-EXP info:eu-repo/semantics/openAccess All rights reserved http://www.europeana.eu/rights/rr-f/ 3.103 2019 COMPUTER SCIENCE, THEORY & METHODS 23 / 108 = 0.213 2019 Q1 T1 OPTICS 23 / 97 = 0.237 2019 Q1 T1 COMPUTER SCIENCE, SOFTWARE ENGINEERING 21 / 107 = 0.196 2019 Q1 T1 ENGINEERING, ELECTRICAL & ELECTRONIC 87 / 265 = 0.328 2019 Q2 T1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE 48 / 136 = 0.353 2019 Q2 T2 1.032 2019 Computer Vision and Pattern Recognition 2019 Q1 Signal Processing 2019 Q1 Electrical and Electronic Engineering 2019 Q1 info:eu-repo/semantics/article info:eu-repo/semantics/acceptedVersion López-Nicolas, Gonzalo Universidad de Zaragoza (orcid)0000-0001-9347-5969 Guerrero, José J. Universidad de Zaragoza (orcid)0000-0001-5209-2267 5007 520 Universidad de Zaragoza Dpto. Informát.Ingenie.Sistms. Área Ingen.Sistemas y Automát. 87 (2019), 76-96 Image vis. comput. IMAGE AND VISION COMPUTING 0262-8856 1385708 http://zaguan.unizar.es/record/149147/files/texto_completo.pdf Postprint 2415371 http://zaguan.unizar.es/record/149147/files/texto_completo.jpg?subformat=icon icon Postprint oai:zaguan.unizar.es:149147 articulos driver 2025-10-17-14:11:31 ARTICLE