Real-Time Object Pose Estimation with Pose Interpreter Networks
Jimmy Wu, Bolei Zhou, Rebecca Russell, Vincent Kee, Syler Wagner, Mitchell Hebert, Antonio Torralba, David M.S. Johnson
Abstract
In this work, we introduce pose interpreter networks for 6-DoF object pose estimation. In contrast to other CNN-based approaches to pose estimation that require expensively annotated object pose data, our pose interpreter network is trained entirely on synthetic pose data. We use object masks as an intermediate representation to bridge real and synthetic. We show that when combined with a segmentation model trained on RGB images, our synthetically trained pose interpreter network is able to generalize to real data. Our end-to-end system for object pose estimation runs in real-time (20 Hz) on live RGB data, without using depth information or ICP refinement.
BibTeX
@inproceedings{iros2018_realtimeobjectpo,
title = {Real-Time Object Pose Estimation with Pose Interpreter Networks},
author = {Jimmy Wu and Bolei Zhou and Rebecca Russell and Vincent Kee and Syler Wagner and Mitchell Hebert and Antonio Torralba and David M.S. Johnson},
booktitle = {IROS 2018},
year = {2018}
}