  Recent progress in computer vision has been dominated by deep neural networks
trained over large amounts of labeled data. Collecting such datasets is however
a tedious, often impossible task; hence a surge in approaches relying solely on
synthetic data for their training. For depth images however, discrepancies with
real scans still noticeably affect the end performance. We thus propose an
end-to-end framework which simulates the whole mechanism of these devices,
generating realistic depth data from 3D models by comprehensively modeling
vital factors e.g. sensor noise, material reflectance, surface geometry. Not
only does our solution cover a wider range of sensors and achieve more
realistic results than previous methods, assessed through extended evaluation,
but we go further by measuring the impact on the training of neural networks
for various recognition tasks; demonstrating how our pipeline seamlessly
integrates such architectures and consistently enhances their performance.
