Electronic control units (ECUs) are essential for many automobile components,
e.g. engine, anti-lock braking system (ABS), steering and airbags. For some
products, the 3D pose of each single ECU needs to be determined during
series production. Deep learning approaches can not easily be applied to this
problem, because labeled training data is not available in sufficient numbers.
Thus, we train state-of-the-art artificial neural networks (ANNs) on purely
synthetic training data, which is automatically created from a single CAD file.
By randomizing parameters during rendering of training images, we enable
inference on RGB images of a real sample part. In contrast to classic image
processing approaches, this data-driven approach poses only few requirements
regarding the measurement setup and transfers to related use cases with little