|
|
|
|
| LEADER |
01000caa a22002652c 4500 |
| 001 |
NLM252592115 |
| 003 |
DE-627 |
| 005 |
20250219030431.0 |
| 007 |
cr uuu---uuuuu |
| 008 |
231224s2014 xx |||||o 00| ||eng c |
| 024 |
7 |
|
|a 10.1109/TPAMI.2013.235
|2 doi
|
| 028 |
5 |
2 |
|a pubmed25n0841.xml
|
| 035 |
|
|
|a (DE-627)NLM252592115
|
| 035 |
|
|
|a (NLM)26353333
|
| 040 |
|
|
|a DE-627
|b ger
|c DE-627
|e rakwb
|
| 041 |
|
|
|a eng
|
| 100 |
1 |
|
|a Wang, Kangkan
|e verfasserin
|4 aut
|
| 245 |
1 |
2 |
|a A Two-Stage Framework for 3D Face Reconstruction from RGBD Images
|
| 264 |
|
1 |
|c 2014
|
| 336 |
|
|
|a Text
|b txt
|2 rdacontent
|
| 337 |
|
|
|a ƒaComputermedien
|b c
|2 rdamedia
|
| 338 |
|
|
|a ƒa Online-Ressource
|b cr
|2 rdacarrier
|
| 500 |
|
|
|a Date Completed 09.03.2016
|
| 500 |
|
|
|a Date Revised 10.09.2015
|
| 500 |
|
|
|a published: Print
|
| 500 |
|
|
|a Citation Status MEDLINE
|
| 520 |
|
|
|a This paper proposes a new approach for 3D face reconstruction with RGBD images from an inexpensive commodity sensor. The challenges we face are: 1) substantial random noise and corruption are present in low-resolution depth maps; and 2) there is high degree of variability in pose and face expression. We develop a novel two-stage algorithm that effectively maps low-quality depth maps to realistic face models. Each stage is targeted toward a certain type of noise. The first stage extracts sparse errors from depth patches through the data-driven local sparse coding, while the second stage smooths noise on the boundaries between patches and reconstructs the global shape by combining local shapes using our template-based surface refinement. Our approach does not require any markers or user interaction. We perform quantitative and qualitative evaluations on both synthetic and real test sets. Experimental results show that the proposed approach is able to produce high-resolution 3D face models with high accuracy, even if inputs are of low quality, and have large variations in viewpoint and face expression
|
| 650 |
|
4 |
|a Journal Article
|
| 650 |
|
4 |
|a Research Support, Non-U.S. Gov't
|
| 700 |
1 |
|
|a Wang, Xianwang
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Pan, Zhigeng
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Liu, Kai
|e verfasserin
|4 aut
|
| 773 |
0 |
8 |
|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 36(2014), 8 vom: 01. Aug., Seite 1493-504
|w (DE-627)NLM098212257
|x 1939-3539
|7 nnas
|
| 773 |
1 |
8 |
|g volume:36
|g year:2014
|g number:8
|g day:01
|g month:08
|g pages:1493-504
|
| 856 |
4 |
0 |
|u http://dx.doi.org/10.1109/TPAMI.2013.235
|3 Volltext
|
| 912 |
|
|
|a GBV_USEFLAG_A
|
| 912 |
|
|
|a SYSFLAG_A
|
| 912 |
|
|
|a GBV_NLM
|
| 912 |
|
|
|a GBV_ILN_350
|
| 951 |
|
|
|a AR
|
| 952 |
|
|
|d 36
|j 2014
|e 8
|b 01
|c 08
|h 1493-504
|