@InProceedings{BatistadaCunhaSanValMagTei:2020:StImDo,
author = "Batista da Cunha, Kelvin and dos Santos Brito, Caio Jos{\'e} and
Valen{\c{c}}a da Rocha Martins Albuquerque, Lucas and
Magalh{\~a}es Sim{\~o}es, Francisco Paulo and Teichrieb,
Veronica",
affiliation = "Voxar Labs, Centro de Inform{\'a}tica, Universidade Federal de
Pernambuco and Voxar Labs, Centro de Inform{\'a}tica,
Universidade Federal de Pernambuco and Voxar Labs, Centro de
Inform{\'a}tica, Universidade Federal de Pernambuco and Curso
T{\'e}cnico em Inform{\'a}tica para Internet, Instituto Federal
de Pernambuco, Campus Belo Jardim and Voxar Labs, Centro de
Inform{\'a}tica, Universidade Federal de Pernambuco",
title = "A Study on the Impact of Domain Randomization for Monocular Deep
6DoF Pose Estimation",
booktitle = "Proceedings...",
year = "2020",
editor = "Musse, Soraia Raupp and Cesar Junior, Roberto Marcondes and
Pelechano, Nuria and Wang, Zhangyang (Atlas)",
organization = "Conference on Graphics, Patterns and Images, 33. (SIBGRAPI)",
publisher = "IEEE Computer Society",
address = "Los Alamitos",
keywords = "Pose Estimation, Deep Learning, Domain Randomization.",
abstract = "In this work, we apply domain randomization to synthetic images
and train deep 6DoF monocular RGB pose estimation models to work
on a real object. We compare 19 models trained with different
combinations of synthetic and real data (fully synthetic, fully
real, initially synthetic and supplemented with real, and a
real-synthetic randomized mix). By gradually decreasing the amount
of real data used, we show it is possible for deep 6DoF detection
to obtain superior results while using less real data (which is
harder to obtain) and suggest the best approach to train a model
with synthetic data. Our method is validated using a textureless
3D printed object, as the textureless category is a challenging,
common open problem in itself. A real and a synthetic dataset
generated for this work, totalling over 24,800 annotated frames,
are also made public. We also show that synthetic, randomized data
can help generalize a model by training it to handle challenges
such as illumination changes and fast motion. Finally, we also
evaluate how a model trained for one camera sensor works with a
different one, and show that synthetic simulations of real cameras
can help overcoming this challenge.",
conference-location = "Porto de Galinhas (virtual)",
conference-year = "7-10 Nov. 2020",
doi = "10.1109/SIBGRAPI51738.2020.00052",
url = "http://dx.doi.org/10.1109/SIBGRAPI51738.2020.00052",
language = "en",
ibi = "8JMKD3MGPEW34M/43BG2TB",
url = "http://urlib.net/ibi/8JMKD3MGPEW34M/43BG2TB",
targetfile = "108.pdf",
urlaccessdate = "2024, May 02"
}