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Deep learning methods typically require vast amounts of training data toreach their full potential. While some publicly available datasets exists,domain specific data always needs to be collected and manually labeled, anexpensive, time consuming and error prone process. Training with synthetic datais therefore very lucrative, as dataset creation and labeling comes for free.We propose a novel method for creating purely synthetic training data forobject detection. We leverage a large dataset of 3D background models anddensely render them using full domain randomization. This yields backgroundimages with realistic shapes and texture on top of which we render the objectsof interest. During training, the data generation process follows a curriculumstrategy guaranteeing that all foreground models are presented to the networkequally under all possible poses and conditions with increasing complexity. Asa result, we entirely control the underlying statistics and we create optimaltraining samples at every stage of training. Using a set of 64 retail objects,we demonstrate that our simple approach enables the training of detectors thatoutperform models trained with real data on a challenging evaluation dataset.

An Annotation Saved is an Annotation Earned: Using Fully Synthetic Training for Object Instance Detection