1 research outputs found
Style-transfer GANs for bridging the domain gap in synthetic pose estimator training
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 non-trivial 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 pixel-level 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 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