  We present a deep neural network based method for the retrieval of watermarks
from images of 3D printed objects. To deal with the variability of all possible
3D printing and image acquisition settings we train the network with synthetic
data. The main simulator parameters such as texture, illumination and camera
position are dynamically randomized in non-realistic ways, forcing the neural
network to learn the intrinsic features of the 3D printed watermarks. At the
end of the pipeline, the watermark, in the form of a two-dimensional bit array,
is retrieved through a series of simple image processing and statistical
operations applied on the confidence map generated by the neural network. The
results demonstrate that the inclusion of synthetic DR data in the training set
increases the generalization power of the network, which performs better on
images from previously unseen 3D printed objects. We conclude that in our
application domain of information retrieval from 3D printed objects, where
access to the exact CAD files of the printed objects can be assumed, one can
use inexpensive synthetic data to enhance neural network training, reducing the
need for the labour intensive process of creating large amounts of hand
labelled real data or the need to generate photorealistic synthetic data.
