Rojtberg, Pavel ; Pollabauer, Thomas ; Kuijper, Arjan (2020)
Style-transfer GANs for bridging the domain gap in synthetic pose estimator training.
International Conference on Artificial Intelligence and Virtual Reality (AIVR 2020). virtual Conference (14.-18.12.)
doi: 10.1109/AIVR50618.2020.00039
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Kurzbeschreibung (Abstract)

Given the dependency of current CNN architectures on a large training set, the possibility of using synthetic data is alluring as it allows generating a virtually infinite amount of labeled training data. However, producing such data is a nontrivial task as current CNN architectures are sensitive to the domain gap between real and synthetic data.We propose to adopt general-purpose GAN models for pixellevel image translation, allowing to formulate the domain gap itself as a learning problem. The obtained models are then used either during training or inference to bridge the domain gap. Here, we focus on training the single-stage YOLO6D [20] object pose estimator on synthetic CAD geometry only, where not even approximate surface information is available. When employing paired GAN models, we use an edge-based intermediate domain and introduce different mappings to represent the unknown surface properties.Our evaluation shows a considerable improvement in model performance when compared to a model trained with the same degree of domain randomization, while requiring only very little additional effort.

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