@inproceedings{tekin2018real-time,
author = {Tekin, Bugra and Sinha, Sudipta and Fua, Pascal},
title = {Real-Time Seamless Single Shot 6D Object Pose Prediction},
booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2018},
year = {2018},
month = {June},
abstract = {We propose a single-shot approach for simultaneously detecting an object in an RGB image and predicting its 6D pose without requiring multiple stages or having to examine multiple hypotheses. Unlike a recently proposed single-shot technique for this task [Kehl et al. 2017] that only predicts an approximate 6D pose that must then be refined, ours is accurate enough not to require additional post-processing. As a result, it is much faster – 50 fps on a Titan X (Pascal) GPU – and more suitable for real-time processing. The key component of our method is a new CNN architecture inspired by YOLO [Redmon et al. 2016, Redmon and Farhadi 2017] that directly predicts the 2D image locations of the projected vertices of the object’s 3D bounding box. The object’s 6D pose is then estimated using a PnP algorithm. For single object and multiple object pose estimation on the LINEMOD and OCCLUSION datasets, our approach substantially outperforms other recent CNN-based approaches [Kehl et al. 2017, Rad and Lepetit 2017] when they are all used without post processing. During post-processing, a pose refinement step can be used to boost the accuracy of these two methods, but at 10 fps or less, they are much slower than our method.},
url = {http://approjects.co.za/?big=en-us/research/publication/real-time-seamless-single-shot-6d-object-pose-prediction/},
note = {arXiv},
}