000089706 001__ 89706 000089706 005__ 20230728141557.0 000089706 0247_ $$2doi$$a10.3390/s20072066 000089706 0248_ $$2sideral$$a117843 000089706 037__ $$aART-2020-117843 000089706 041__ $$aeng 000089706 100__ $$0(orcid)0000-0003-2674-4844$$aBerenguel-Baeta, Bruno$$uUniversidad de Zaragoza 000089706 245__ $$aOmniSCV: An omnidirectional synthetic image generator for computer vision 000089706 260__ $$c2020 000089706 5060_ $$aAccess copy available to the general public$$fUnrestricted 000089706 5203_ $$aOmnidirectional and 360º images are becoming widespread in industry and in consumer society, causing omnidirectional computer vision to gain attention. Their wide field of view allows the gathering of a great amount of information about the environment from only an image. However, the distortion of these images requires the development of specific algorithms for their treatment and interpretation. Moreover, a high number of images is essential for the correct training of computer vision algorithms based on learning. In this paper, we present a tool for generating datasets of omnidirectional images with semantic and depth information. These images are synthesized from a set of captures that are acquired in a realistic virtual environment for Unreal Engine 4 through an interface plugin. We gather a variety of well-known projection models such as equirectangular and cylindrical panoramas, different fish-eye lenses, catadioptric systems, and empiric models. Furthermore, we include in our tool photorealistic non-central-projection systems as non-central panoramas and non-central catadioptric systems. As far as we know, this is the first reported tool for generating photorealistic non-central images in the literature. Moreover, since the omnidirectional images are made virtually, we provide pixel-wise information about semantics and depth as well as perfect knowledge of the calibration parameters of the cameras. This allows the creation of ground-truth information with pixel precision for training learning algorithms and testing 3D vision approaches. To validate the proposed tool, different computer vision algorithms are tested as line extractions from dioptric and catadioptric central images, 3D Layout recovery and SLAM using equirectangular panoramas, and 3D reconstruction from non-central panoramas. 000089706 536__ $$9info:eu-repo/grantAgreement/ES/MICINN/RTI2018-096903-B-100$$9info:eu-repo/grantAgreement/ES/UZ/JIUZ-2019-TEC-03 000089706 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/ 000089706 590__ $$a3.576$$b2020 000089706 591__ $$aINSTRUMENTS & INSTRUMENTATION$$b14 / 64 = 0.219$$c2020$$dQ1$$eT1 000089706 591__ $$aCHEMISTRY, ANALYTICAL$$b26 / 83 = 0.313$$c2020$$dQ2$$eT1 000089706 591__ $$aENGINEERING, ELECTRICAL & ELECTRONIC$$b82 / 273 = 0.3$$c2020$$dQ2$$eT1 000089706 592__ $$a0.636$$b2020 000089706 593__ $$aAnalytical Chemistry$$c2020$$dQ2 000089706 593__ $$aAtomic and Molecular Physics, and Optics$$c2020$$dQ2 000089706 593__ $$aBiochemistry$$c2020$$dQ2 000089706 593__ $$aMedicine (miscellaneous)$$c2020$$dQ2 000089706 593__ $$aInformation Systems$$c2020$$dQ2 000089706 593__ $$aInstrumentation$$c2020$$dQ2 000089706 593__ $$aElectrical and Electronic Engineering$$c2020$$dQ2 000089706 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion 000089706 700__ $$0(orcid)0000-0002-8479-1748$$aBermudez-Cameo, Jesús$$uUniversidad de Zaragoza 000089706 700__ $$0(orcid)0000-0001-5209-2267$$aGuerrero, José J.$$uUniversidad de Zaragoza 000089706 7102_ $$15007$$2520$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Ingen.Sistemas y Automát. 000089706 773__ $$g20, 7 (2020), 2066 [25 pp.]$$pSensors$$tSensors$$x1424-8220 000089706 8564_ $$s6900431$$uhttps://zaguan.unizar.es/record/89706/files/texto_completo.pdf$$yVersión publicada 000089706 8564_ $$s28961$$uhttps://zaguan.unizar.es/record/89706/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada 000089706 909CO $$ooai:zaguan.unizar.es:89706$$particulos$$pdriver 000089706 951__ $$a2023-07-28-11:57:37 000089706 980__ $$aARTICLE