Please use this identifier to cite or link to this item:
https://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) (3 Sep 2018 - 6 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. |
Published version: | http://bmvc2018.org/programmedetail.html |
Appears in Collections: | Aurora harvest 4 Computer Science publications |
Files in This Item:
File | Description | Size | Format | |
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hdl_124685.pdf | Published version | 7.6 MB | Adobe PDF | View/Open |
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