2026-09-19T05:39:34Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1196992026-06-06T00:55:43Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Antonio Torralba.
Wu, Jimmy, M. Eng. Massachusetts Institute of Technology
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2018-12-18T19:46:18Z
2018-12-18T19:46:18Z
2018
2018
http://hdl.handle.net/1721.1/119699
1078151125
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 39-45).
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 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.
by Jimmy Wu.
M.Eng.
45 pages
eng
Massachusetts Institute of Technology
MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Robotic object pose estimation with deep neural networks
Thesis
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