|
|
|
|
LEADER |
01000naa a22002652 4500 |
001 |
NLM286368439 |
003 |
DE-627 |
005 |
20231225051539.0 |
007 |
cr uuu---uuuuu |
008 |
231225s2019 xx |||||o 00| ||eng c |
024 |
7 |
|
|a 10.1109/TPAMI.2018.2839602
|2 doi
|
028 |
5 |
2 |
|a pubmed24n0954.xml
|
035 |
|
|
|a (DE-627)NLM286368439
|
035 |
|
|
|a (NLM)29994300
|
040 |
|
|
|a DE-627
|b ger
|c DE-627
|e rakwb
|
041 |
|
|
|a eng
|
100 |
1 |
|
|a Xu, Dan
|e verfasserin
|4 aut
|
245 |
1 |
0 |
|a Monocular Depth Estimation Using Multi-Scale Continuous CRFs as Sequential Deep Networks
|
264 |
|
1 |
|c 2019
|
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 Revised 20.11.2019
|
500 |
|
|
|a published: Print-Electronic
|
500 |
|
|
|a Citation Status PubMed-not-MEDLINE
|
520 |
|
|
|a Depth cues have been proved very useful in various computer vision and robotic tasks. This paper addresses the problem of monocular depth estimation from a single still image. Inspired by the effectiveness of recent works on multi-scale convolutional neural networks (CNN), we propose a deep model which fuses complementary information derived from multiple CNN side outputs. Different from previous methods using concatenation or weighted average schemes, the integration is obtained by means of continuous Conditional Random Fields (CRFs). In particular, we propose two different variations, one based on a cascade of multiple CRFs, the other on a unified graphical model. By designing a novel CNN implementation of mean-field updates for continuous CRFs, we show that both proposed models can be regarded as sequential deep networks and that training can be performed end-to-end. Through an extensive experimental evaluation, we demonstrate the effectiveness of the proposed approach and establish new state of the art results for the monocular depth estimation task on three publicly available datasets, i.e., NYUD-V2, Make3D and KITTI
|
650 |
|
4 |
|a Journal Article
|
700 |
1 |
|
|a Ricci, Elisa
|e verfasserin
|4 aut
|
700 |
1 |
|
|a Wanli Ouyang
|e verfasserin
|4 aut
|
700 |
1 |
|
|a Xiaogang Wang
|e verfasserin
|4 aut
|
700 |
1 |
|
|a Sebe, Nicu
|e verfasserin
|4 aut
|
773 |
0 |
8 |
|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 41(2019), 6 vom: 26. Juni, Seite 1426-1440
|w (DE-627)NLM098212257
|x 1939-3539
|7 nnns
|
773 |
1 |
8 |
|g volume:41
|g year:2019
|g number:6
|g day:26
|g month:06
|g pages:1426-1440
|
856 |
4 |
0 |
|u http://dx.doi.org/10.1109/TPAMI.2018.2839602
|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 41
|j 2019
|e 6
|b 26
|c 06
|h 1426-1440
|