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Eric Cameracci

3 accepted papers

2021

Self-Supervised Real-to-Sim Scene Generation

ICCV 2021poster

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. Synthetic data generation, however, can itself be prohibitively expensive when domain experts have to manually and painstak…

Cited by 28PDFScholar
2019

Meta-Sim: Learning to Generate Synthetic Datasets

ICCV 2019oral

Training models to high-end performance requires availability of large labeled datasets, which are expensive to get. The goal of our work is to automatically synthesize labeled datasets that are relevant for a downstream task. We propose Meta-Sim, which learns a generative model of synthetic scenes,…

Cited by 316PDFScholar
2019

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

ICRA 2019poster

We present structured domain randomization (SDR), a variant of domain randomization (DR) that takes into account the structure of the scene in order to add context to the generated data. In contrast to DR, which places objects and distractors randomly according to a uniform probability distribution,…

Cited by 228SourceScholar