Conference item
Moving SLAM: fully unsupervised deep learning in non-rigid scenes
- Abstract:
-
We propose a new deep learning framework to decompose monocular videos into 3D geometry (camera pose and depth), moving objects, and their motions, with no supervision. We build upon the idea of view synthesis, which uses classical camera geometry to re-render a source image from a different point-of-view to obtain supervisory signals, specified by a predicted relative 6-degree-of-freedom pose and depth map. However, the typical view synthesis equations rely on a strong assumption: that objec...
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- Publication status:
- Published
- Peer review status:
- Peer reviewed
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Bibliographic Details
- Publisher:
- IEEE Publisher's website
- Host title:
- Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2021)
- Pages:
- 4611-4617
- Publication date:
- 2021-09-27
- Event title:
- IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2021)
- Event location:
- Prague, Czech Republic
- Event website:
- https://www.iros2021.org/
- Event start date:
- 2021-09-27
- Event end date:
- 2021-10-01
- DOI:
- EISSN:
-
2153-0866
- ISSN:
-
2153-0858
- EISBN:
- 978-1-6654-1714-3
- ISBN:
- 978-1-6654-1715-0
Item Description
- Language:
- English
- Keywords:
- Pubs id:
-
1233294
- Local pid:
- pubs:1233294
- Deposit date:
- 2022-01-25
Terms of use
- Copyright holder:
- IEEE
- Copyright date:
- 2021
- Rights statement:
- © 2021 IEEE
- Notes:
- This paper was presented at the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2021), 27th September- 1st October 2021, Czech Republic, Prague. This is the accepted manuscript version of the article. The final version is available online from IEEE at: https://doi.org/10.1109/IROS51168.2021.9636075
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