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 Reconstruction3014583UNINA05609nam 2201357z- 450 991058021400332120220706(CKB)5690000000011948(oapen)https://directory.doabooks.org/handle/20.500.12854/87529(oapen)doab87529(EXLCZ)99569000000001194820202207d2022 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierAdvances in Automated Driving SystemsBaselMDPI - Multidisciplinary Digital Publishing Institute20221 online resource (294 p.)3-0365-4503-4 3-0365-4504-2 Electrification, automation of vehicle control, digitalization and new mobility are the mega-trends in automotive engineering, and they are strongly connected. While many demonstrations for highly automated vehicles have been made worldwide, many challenges remain in bringing automated vehicles to the market for private and commercial use. The main challenges are as follows: reliable machine perception; accepted standards for vehicle-type approval and homologation; verification and validation of the functional safety, especially at SAE level 3+ systems; legal and ethical implications; acceptance of vehicle automation by occupants and society; interaction between automated and human-controlled vehicles in mixed traffic; human-machine interaction and usability; manipulation, misuse and cyber-security; the system costs of hard- and software and development efforts. This Special Issue was prepared in the years 2021 and 2022 and includes 15 papers with original research related to recent advances in the aforementioned challenges. The topics of this Special Issue cover: Machine perception for SAE L3+ driving automation; Trajectory planning and decision-making in complex traffic situations; X-by-Wire system components; Verification and validation of SAE L3+ systems; Misuse, manipulation and cybersecurity; Human-machine interactions, driver monitoring and driver-intention recognition; Road infrastructure measures for the introduction of SAE L3+ systems; Solutions for interactions between human- and machine-controlled vehicles in mixed traffic.History of engineering & technologybicsscTechnology: general issuesbicsscacceptanceadaptive controladaptive cruise controlADASadvanced driver assistant systems (ADAS)automated drivingautomated driving (AD)automated driving systemsautomationautonomous conflict managementautonomous driftingautonomous vehiclescalibration methodconnected and automated vehicleconvolutional neural networkcooperative perceptiondecompositiondeep learningdigital twindriver assistance systemdriver drowsinessdriving schooldriving simulatorECG signaledge cloudexpression of trustfault tree analysisframework developmentground truthheart rate variabilityilluminationinformed machine learninginstance segmentationinverse gamma correctionITSlane detectionMask R-CNNmodel predictive control (MPC)modular safety approvalmodular testingmulti-layer perceptronn/aNASA TLXpedestrian custom datasetphysical perception modelphysics-guided reinforcement learningradar sensorreference measurementsafetysafety validationscenario-based testingsensor fusionsimulationsimulation and modelingsimulation and modellingsimulator case studysoftware frameworkstate predictionsuccessive linearizationsystem usability scalethrottle predictiontraffic evaluationtraffic sign recognition systemtraffic signstransfer learningU-SpaceUAVUGVUTMvarying road surfacesvehicle dynamicsvehicle motion controlvirtual sensor modelvirtual test and validationvirtual validationwavelet scalogramwheel loadersHistory of engineering & technologyTechnology: general issuesEichberger Arnoedt1328211Szalay ZsoltedtFellendorf MartinedtLiu HenryedtEichberger ArnoothSzalay ZsoltothFellendorf MartinothLiu HenryothBOOK9910580214003321Advances in Automated Driving Systems3038431UNINA