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231225s2020 xx |||||o 00| ||eng c |
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|a 10.1109/TPAMI.2019.2915229
|2 doi
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| 028 |
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|a pubmed24n0989.xml
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|a (DE-627)NLM296902721
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|a DE-627
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|e rakwb
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| 041 |
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|a eng
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| 100 |
1 |
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|a Xu, Lan
|e verfasserin
|4 aut
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| 245 |
1 |
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|a UnstructuredFusion
|b Realtime 4D Geometry and Texture Reconstruction Using Commercial RGBD Cameras
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| 264 |
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|c 2020
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|2 rdacarrier
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| 500 |
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|a Date Completed 12.02.2021
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| 500 |
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|a Date Revised 12.02.2021
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| 500 |
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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 A high-quality 4D geometry and texture reconstruction for human activities usually requires multiview perceptions via highly structured multi-camera setup, where both the specifically designed cameras and the tedious pre-calibration restrict the popularity of professional multi-camera systems for daily applications. In this paper, we propose UnstructuredFusion, a practicable realtime markerless human performance capture method using unstructured commercial RGBD cameras. Along with the flexible hardware setup using simply three unstructured RGBD cameras without any careful pre-calibration, the challenge 4D reconstruction through multiple asynchronous videos is solved by proposing three novel technique contributions, i.e., online multi-camera calibration, skeleton warping based non-rigid tracking, and temporal blending based atlas texturing. The overall insights behind lie in the solid global constraints of human body and human motion which are modeled by the skeleton and the skeleton warping, respectively. Extensive experiments such as allocating three cameras flexibly in a handheld way demonstrate that the proposed UnstructuredFusion achieves high-quality 4D geometry and texture reconstruction without tiresome pre-calibration, liberating the cumbersome hardware and software restrictions in conventional structured multi-camera system, while eliminating the inherent occlusion issues of the single camera setup
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| 650 |
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|a Journal Article
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| 650 |
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4 |
|a Research Support, Non-U.S. Gov't
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| 700 |
1 |
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|a Su, Zhuo
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Han, Lei
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Yu, Tao
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Liu, Yebin
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Fang, Lu
|e verfasserin
|4 aut
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| 773 |
0 |
8 |
|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 42(2020), 10 vom: 07. Okt., Seite 2508-2522
|w (DE-627)NLM098212257
|x 1939-3539
|7 nnns
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| 773 |
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|g volume:42
|g year:2020
|g number:10
|g day:07
|g month:10
|g pages:2508-2522
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| 856 |
4 |
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|u http://dx.doi.org/10.1109/TPAMI.2019.2915229
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