  Domain randomisation is a very popular method for visual sim-to-real transfer
in robotics, due to its simplicity and ability to achieve transfer without any
real-world images at all. Nonetheless, a number of design choices must be made
to achieve optimal transfer. In this paper, we perform a comprehensive
benchmarking study on these different choices, with two key experiments
evaluated on a real-world object pose estimation task. First, we study the
rendering quality, and find that a small number of high-quality images is
superior to a large number of low-quality images. Second, we study the type of
randomisation, and find that both distractors and textures are important for
generalisation to novel environments.
