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

CAD2Real: Deep learning with domain randomization of CAD data for 3D pose estimation of electronic control unit housings