Apr 18, 2018 · We present a system for training deep neural networks for object detection using synthetic images. To handle the variability in real-world data,
We have demonstrated that domain randomization (DR) is an effective technique to bridge the reality gap. Using synthetic DR data alone, we have trained a neural ...
This work presents a system for training deep neural networks for object detection using synthetic images that relies upon the technique of domain ...
We present a system for training deep neural networks for object detection using synthetic images. To handle the variability in real-world data,
We present a system for training deep neural networks for object detection using synthetic images. To handle the variability in real-world data, ...
We present a system for training deep neural networks for object detection using synthetic images. To handle the variability in real-world data, the system ...
Oct 23, 2018 · We present structured domain randomization (SDR), a variant of domain randomization (DR) that takes into account the structure and context of the scene.
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In this study we consider simulation-based approaches which consist of an automated process to render and accurately annotate numerous synthetic images out of ...
Training Deep Networks with Synthetic Data: Bridging the Reality Gap by Domain Randomization ... Authors: Jonathan Tremblay; Aayush Prakash; David Acuna; Mark ...
Training Deep Networks with Synthetic Data: Bridging the Reality Gap by Domain Randomization · Computer Science. 2018 IEEE/CVF Conference on Computer Vision and…