Feature Mapping for Learning Fast and Accurate 3D Pose Inference from Synthetic Images

Mahdi Rad, Markus Oberweger, Vincent Lepetit

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review

Original language	English
Title of host publication	Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
Publication status	Published - 2018
Event	IEEE Conference on Computer Vision and Pattern Recognition, 2016: CVPR 2018 - Salt Lake City, United States Duration: 18 Jun 2018 → 22 Jun 2018

Conference	IEEE Conference on Computer Vision and Pattern Recognition, 2016
Abbreviated title	CVPR 2018
Country/Territory	United States
City	Salt Lake City
Period	18/06/18 → 22/06/18

Cite this

@inproceedings{e3db80db0ff846068521d2f8a5c4f15e,

title = "Feature Mapping for Learning Fast and Accurate 3D Pose Inference from Synthetic Images",

author = "Mahdi Rad and Markus Oberweger and Vincent Lepetit",

year = "2018",

language = "English",

booktitle = "Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition",

note = "IEEE Conference on Computer Vision and Pattern Recognition, 2016 : CVPR 2018, CVPR 2018 ; Conference date: 18-06-2018 Through 22-06-2018",

}

TY - GEN

T1 - Feature Mapping for Learning Fast and Accurate 3D Pose Inference from Synthetic Images

AU - Rad, Mahdi

AU - Oberweger, Markus

AU - Lepetit, Vincent

PY - 2018

Y1 - 2018

M3 - Conference paper

BT - Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition

T2 - IEEE Conference on Computer Vision and Pattern Recognition, 2016

Y2 - 18 June 2018 through 22 June 2018

ER -