Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/124685
Type: Conference paper
Title: Real-time monocular object instance 6D pose estimation
Author: Do, T.
Pham, T.
Cai, M.
Reid, I.
Citation: Proceedings of the 29th British Machine Vision Conference (BMVC 2018), 2019 / pp.1-12
Publisher: BMVC Press
Issue Date: 2019
Conference Name: British Machine Vision Conference (BMVC) (03 Sep 2018 - 06 Sep 2018 : Newcastle upon Tyne, UK)
Statement of
Responsibility: 
Thanh-Toan Do, Trung Pham, Ming Cai, Ian Reid
Abstract: In this work, we present, LieNet, a novel deep learning framework that simultaneously detects, segments multiple object instances, and estimates their 6D poses from a single RGB image without requiring additional post-processing. Our system is accurate and fast (∼10 fps), which is well suited for real-time applications. In particular, LieNet detects and segments object instances in the image analogous to modern instance segmentation networks such as Mask R-CNN, but contains a novel additional sub-network for 6D pose estimation. LieNet estimates the rotation matrix of an object by regressing a Lie algebra based rotation representation, and estimates the translation vector by predicting the distance of the object to the camera center. The experiments on two standard pose benchmarking datasets show that LieNet greatly outperforms other recent CNN based pose prediction methods when they are used with monocular images and without post-refinements.
Rights: © 2018. The copyright of this document resides with its authors. It may be distributed unchanged freely in print or electronic forms.
RMID: 1000000490
Published version: http://bmvc2018.org/programmedetail.html
Appears in Collections:Computer Science publications

Files in This Item:
File Description SizeFormat 
hdl_124685.pdfPublished version7.6 MBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.