05718nam 2201417z- 450 991063998510332120231214133044.03-0365-6062-9(CKB)5470000001633503(oapen)https://directory.doabooks.org/handle/20.500.12854/95825(EXLCZ)99547000000163350320202301d2022 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierAugmented Reality, Virtual Reality & Semantic 3D ReconstructionBaselMDPI - Multidisciplinary Digital Publishing Institute20221 electronic resource (304 p.)3-0365-6061-0 Augmented reality is a key technology that will facilitate a major paradigm shift in the way users interact with data and has only just recently been recognized as a viable solution for solving many critical needs. In practical terms, this innovation can be used to visualize data from hundreds of sensors simultaneously, overlaying relevant and actionable information over your environment through a headset. Semantic 3D reconstruction unlocks the promise of AR technology, possessing a far greater availability of semantic information. Although, there are several methods currently available as post-processing approaches to extract semantic information from the reconstructed 3D models, the results obtained results have been uncertain and evenly incorrect. Thus, it is necessary to explore or develop a novel 3D reconstruction approach to automatically recover 3D geometry model and obtained semantic information simultaneously. The rapid advent of deep learning brought new opportunities to the field of semantic 3D reconstruction from photo collections. Deep learning-based methods are not only able to extract semantic information but can also enhance fundamental techniques in semantic 3D reconstruction, techniques which include feature matching or tracking, stereo matching, camera pose estimation, and use of multi-view stereo methods. Moreover, deep learning techniques can be used to extract priors from photo collections, and this obtained information can in turn improve the quality of 3D reconstruction.Technology: general issuesbicsscHistory of engineering & technologybicsscfeature trackingsuperpixelstructure from motionthree-dimensional reconstructionlocal featuremulti-view stereoconstruction hazardsafety educationphotorealityvirtual realityanatomizationaudio classificationolfactory displaydeep learningtransfer learninginception modelaugmented realityhigher educationscientific productionweb of sciencebibliometric analysisscientific mappingapplications in subject areasinteractive learning environments3P modelprimary educationeducational technologymobile lip reading systemlightweight neural networkface correctionvirtual reality (VR)computer visionprojection mapping3D face modelsuper-resolutionradial curveDynamic Time Warpingsemantic 3D reconstructioneye-in-hand vision systemrobotic manipulatorprobabilistic fusiongraph-based refinement3D modelling3D representationgame enginelaser scanningpanoramic photographysuper-resolution reconstructiongenerative adversarial networksdense convolutional networkstexture lossWGAN-GPorientationpositioningviewpointimage matchingalgorithmtransformationADHDEDAHassessmentcontinuous performance testPhotometric Stereo (PS)3D reconstructionfully convolutional network (FCN)semi-immersive virtual realitychildrencooperative gamesempowermentperceptionmotor planningproblem-solvingarea of interestwayfindingspatial informationone-shot learninggesture recognitionGRENskeleton-based3D compositionpre-visualizationstereo vision360° videoTechnology: general issuesHistory of engineering & technologyLv Zhihanedt1217078Wang Jing-YanedtKumar NeerajedtLloret JaimeedtLv ZhihanothWang Jing-YanothKumar NeerajothLloret JaimeothBOOK9910639985103321Augmented Reality, Virtual Reality & Semantic 3D Reconstruction3014583UNINA