Self-Supervised Real-to-Sim Scene Generation

no code implementations • ICCV 2021 • Aayush Prakash, Shoubhik Debnath, Jean-Francois Lafleche, Eric Cameracci, Gavriel State, Stan Birchfield, Marc T. Law

Synthetic data is emerging as a promising solution to the scalability issue of supervised deep learning, especially when real data are difficult to acquire or hard to annotate.

Graph Generation Scene Generation +3

Sim2SG: Sim-to-Real Scene Graph Generation for Transfer Learning

no code implementations • 28 Sep 2020 • Aayush Prakash, Shoubhik Debnath, Jean Francois Lafleche, Eric Cameracci, Gavriel State, Marc T Law

However, neural network models trained on synthetic data, do not perform well on real data because of the domain gap.

Graph Generation Scene Graph Generation +2

Meta-Sim: Learning to Generate Synthetic Datasets

no code implementations • ICCV 2019 • Amlan Kar, Aayush Prakash, Ming-Yu Liu, Eric Cameracci, Justin Yuan, Matt Rusiniak, David Acuna, Antonio Torralba, Sanja Fidler

Training models to high-end performance requires availability of large labeled datasets, which are expensive to get.

Structured Domain Randomization: Bridging the Reality Gap by Context-Aware Synthetic Data

no code implementations • 23 Oct 2018 • Aayush Prakash, Shaad Boochoon, Mark Brophy, David Acuna, Eric Cameracci, Gavriel State, Omer Shapira, Stan Birchfield

Moreover, synthetic SDR data combined with real KITTI data outperforms real KITTI data alone.

Training Deep Networks with Synthetic Data: Bridging the Reality Gap by Domain Randomization

1 code implementation • 18 Apr 2018 • Jonathan Tremblay, Aayush Prakash, David Acuna, Mark Brophy, Varun Jampani, Cem Anil, Thang To, Eric Cameracci, Shaad Boochoon, Stan Birchfield

We present a system for training deep neural networks for object detection using synthetic images.

Object object-detection +1