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230202 ||| eng |
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|a books978-3-0365-6062-5
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|a 9783036560625
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|a 9783036560618
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|a Lv, Zhihan
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| 245 |
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|a Augmented Reality, Virtual Reality & Semantic 3D Reconstruction
|h Elektronische Ressource
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| 260 |
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|a Basel
|b MDPI - Multidisciplinary Digital Publishing Institute
|c 2022
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| 300 |
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|a 1 electronic resource (304 p.)
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| 653 |
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|a continuous performance test
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|a one-shot learning
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|a assessment
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|a algorithm
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|a skeleton-based
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|a deep learning
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|a super-resolution
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| 653 |
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|a 360° video
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| 653 |
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|a positioning
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| 653 |
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|a History of engineering & technology / bicssc
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| 653 |
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|a empowerment
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| 653 |
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|a multi-view stereo
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| 653 |
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|a local feature
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| 653 |
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|a Technology: general issues / bicssc
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| 653 |
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|a EDAH
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| 653 |
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|a bibliometric analysis
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| 653 |
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|a texture loss
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| 653 |
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|a audio classification
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| 653 |
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|a cooperative games
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| 653 |
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|a virtual reality (VR)
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| 653 |
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|a scientific production
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| 653 |
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|a pre-visualization
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| 653 |
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|a olfactory display
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| 653 |
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|a dense convolutional networks
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| 653 |
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|a game engine
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| 653 |
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|a WGAN-GP
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| 653 |
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|a projection mapping
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| 653 |
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|a 3D composition
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| 653 |
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|a GREN
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| 653 |
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|a computer vision
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| 653 |
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|a anatomization
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| 653 |
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|a problem-solving
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| 653 |
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|a motor planning
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| 653 |
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|a 3P model
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| 653 |
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|a 3D face model
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| 653 |
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|a three-dimensional reconstruction
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| 653 |
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|a semantic 3D reconstruction
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| 653 |
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|a ADHD
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| 653 |
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|a augmented reality
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| 653 |
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|a web of science
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| 653 |
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|a image matching
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| 653 |
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|a 3D reconstruction
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| 653 |
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|a fully convolutional network (FCN)
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| 653 |
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|a mobile lip reading system
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| 653 |
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|a graph-based refinement
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| 653 |
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|a robotic manipulator
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| 653 |
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|a panoramic photography
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| 653 |
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|a stereo vision
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| 653 |
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|a higher education
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| 653 |
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|a spatial information
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|a n/a
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|a transformation
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| 653 |
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|a interactive learning environments
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| 653 |
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|a area of interest
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| 653 |
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|a orientation
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| 653 |
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|a generative adversarial networks
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| 653 |
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|a super-resolution reconstruction
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| 653 |
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|a photoreality
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|a superpixel
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| 653 |
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|a Photometric Stereo (PS)
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| 653 |
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|a children
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| 653 |
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|a virtual reality
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| 653 |
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|a educational technology
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| 653 |
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|a viewpoint
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| 653 |
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|a transfer learning
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| 653 |
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|a Dynamic Time Warping
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| 653 |
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|a perception
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| 653 |
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|a applications in subject areas
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| 653 |
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|a lightweight neural network
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| 653 |
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|a scientific mapping
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| 653 |
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|a laser scanning
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| 653 |
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|a 3D modelling
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| 653 |
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|a primary education
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| 653 |
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|a construction hazard
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| 653 |
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|a feature tracking
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| 653 |
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|a face correction
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| 653 |
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|a gesture recognition
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| 653 |
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|a radial curve
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| 653 |
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|a probabilistic fusion
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| 653 |
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|a eye-in-hand vision system
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| 653 |
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|a wayfinding
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| 653 |
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|a safety education
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| 653 |
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|a 3D representation
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| 653 |
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|a inception model
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| 653 |
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|a semi-immersive virtual reality
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| 653 |
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|a structure from motion
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| 700 |
1 |
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|a Wang, Jing-Yan
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| 700 |
1 |
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|a Kumar, Neeraj
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| 700 |
1 |
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|a Lloret, Jaime
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b DOAB
|a Directory of Open Access Books
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| 500 |
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|a Creative Commons (cc), https://creativecommons.org/licenses/by/4.0/
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| 028 |
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|a 10.3390/books978-3-0365-6062-5
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| 856 |
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|u https://www.mdpi.com/books/pdfview/book/6481
|7 0
|x Verlag
|3 Volltext
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| 856 |
4 |
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|u https://directory.doabooks.org/handle/20.500.12854/95825
|z DOAB: description of the publication
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|a 363
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|a 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.
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