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231225s2021 xx |||||o 00| ||eng c |
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|a 10.1109/TVCG.2020.2975801
|2 doi
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|a DE-627
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|e rakwb
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|a eng
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| 100 |
1 |
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|a Meng, Xiaoxu
|e verfasserin
|4 aut
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| 245 |
1 |
0 |
|a 3D-Kernel Foveated Rendering for Light Fields
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| 264 |
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1 |
|c 2021
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| 336 |
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|a Text
|b txt
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|a ƒaComputermedien
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|2 rdamedia
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|a ƒa Online-Ressource
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| 500 |
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|a Date Revised 01.07.2021
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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| 520 |
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|a Light fields capture both the spatial and angular rays, thus enabling free-viewpoint rendering and custom selection of the focal plane. Scientists can interactively explore pre-recorded microscopic light fields of organs, microbes, and neurons using virtual reality headsets. However, rendering high-resolution light fields at interactive frame rates requires a very high rate of texture sampling, which is challenging as the resolutions of light fields and displays continue to increase. In this article, we present an efficient algorithm to visualize 4D light fields with 3D-kernel foveated rendering (3D-KFR). The 3D-KFR scheme coupled with eye-tracking has the potential to accelerate the rendering of 4D depth-cued light fields dramatically. We have developed a perceptual model for foveated light fields by extending the KFR for the rendering of 3D meshes. On datasets of high-resolution microscopic light fields, we observe 3.47×-7.28× speedup in light field rendering with minimal perceptual loss of detail. We envision that 3D-KFR will reconcile the mutually conflicting goals of visual fidelity and rendering speed for interactive visualization of light fields
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| 650 |
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4 |
|a Journal Article
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| 700 |
1 |
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|a Du, Ruofei
|e verfasserin
|4 aut
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| 700 |
1 |
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|a JaJa, Joseph F
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Varshney, Amitabh
|e verfasserin
|4 aut
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| 773 |
0 |
8 |
|i Enthalten in
|t IEEE transactions on visualization and computer graphics
|d 1996
|g 27(2021), 8 vom: 24. Aug., Seite 3350-3360
|w (DE-627)NLM098269445
|x 1941-0506
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| 773 |
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|g volume:27
|g year:2021
|g number:8
|g day:24
|g month:08
|g pages:3350-3360
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|u http://dx.doi.org/10.1109/TVCG.2020.2975801
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