Conditional Patch-Based Domain Randomization: Improving Texture Domain Randomization Using Natural Image Patches
- Resource Type
- Conference
- Authors
- Ani, Mohammad; Basevi, Hector; Leonardis, Ales
- Source
- 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) Intelligent Robots and Systems (IROS), 2022 IEEE/RSJ International Conference on. :1979-1985 Oct, 2022
- Subject
- Bioengineering
Components, Circuits, Devices and Systems
Computing and Processing
General Topics for Engineers
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Training
Deep learning
Semantic segmentation
Object detection
Data models
Labeling
Task analysis
- Language
- ISSN
- 2153-0866
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 [1] 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.