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

• URL: http://arxiv.org/abs/2009.12312v1
• Date: Fri, 25 Sep 2020 16:08:16 GMT
• Title: CAD2Real: Deep learning with domain randomization of CAD data for 3D pose estimation of electronic control unit housings
• Authors: Simon Baeuerle, Jonas Barth, Elton Renato Tavares de Menezes, Andreas Steimer, Ralf Mikut
• Abstract summary: We train state-of-the-art artificial neural networks (ANNs) on purely synthetic training data. By randomizing parameters during rendering of training images, we enable inference on RGB images of a real sample part.
• Score: 0.20999222360659608
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 development effort.

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