  Recently, the use of synthetic datasets based on game engines has been shown
to improve the performance of several tasks in computer vision. However, these
datasets are typically only appropriate for the specific domains depicted in
computer games, such as urban scenes involving vehicles and people. In this
paper, we present an approach to generate synthetic datasets for object
counting for any domain without the need for photo-realistic techniques
manually generated by expensive teams of 3D artists. We introduce a domain
randomization approach for object counting based on synthetic datasets that are
quick and inexpensive to generate. We deliberately avoid photorealism and
drastically increase the variability of the dataset, producing images with
random textures and 3D transformations, which improves generalization.
Experiments show that our method facilitates good performance on various real
word object counting datasets for multiple domains: people, vehicles, penguins,
and fruit. The source code is available at: https://github.com/enric1994/dr4oc
