Projects per year
Abstract
Using Domain Randomized synthetic data for training deep learning systems is a promising approach for addressing the data and the labeling requirements for supervised techniques to bridge the gap between simulation and the real world. We propose a novel approach for generating and applying class-specific Domain Randomization textures by using randomly cropped image patches from real-world data. In evaluation against the current Domain Randomization texture application techniques, our approach outperforms the highest performing technique by 4.94 AP and 6.71 AP when solving object detection and semantic segmentation tasks on the YCB-M real-world robotics dataset. Our approach is a fast and inexpensive way of generating Domain Randomized textures while avoiding the need to handcraft texture distributions currently being used.
Original language | English |
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Title of host publication | 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) |
Place of Publication | Kyoto, Japan |
Publisher | IEEE |
Pages | 1979-1985 |
ISBN (Electronic) | 978-1-6654-7927-1 |
ISBN (Print) | 978-1-6654-7928-8 |
DOIs | |
Publication status | Published - 26 Dec 2022 |
Event | 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2022 - Kyoto International Conference Center, Kyoto, Japan Duration: 23 Oct 2022 → 27 Oct 2022 https://iros2022.org/ |
Publication series
Name | Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems |
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Publisher | IEEE |
ISSN (Print) | 2153-0858 |
ISSN (Electronic) | 2153-0866 |
Conference
Conference | 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2022 |
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Abbreviated title | IROS 2022 |
Country/Territory | Japan |
City | Kyoto |
Period | 23/10/22 → 27/10/22 |
Internet address |
Bibliographical note
Not yet published as of 20/01/2023Keywords
- Domain randomisation
- Computer vision
- Synthetic data
ASJC Scopus subject areas
- Computer Vision and Pattern Recognition
Fingerprint
Dive into the research topics of 'Conditional patch-based domain randomization: improving texture domain randomization using natural image patches'. Together they form a unique fingerprint.Projects
- 2 Finished
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BURG: Benchmarks for UndeRstanding Grasping
Engineering & Physical Science Research Council
1/11/19 → 31/07/23
Project: Research Councils
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Understanding scenes and events through joint parsing, cognitive reasoning and lifelong learning (Oxford lead)
Engineering & Physical Science Research Council
1/01/16 → 28/02/22
Project: Research Councils