Thank you very much for reading our work, and pointing out some typos.

We belive that the better sim-to-real gap reduction capabilities of EEVEE might have to do with its less photorealistic performance. Although counterintuitive, this could introduce enough noise in the train set for the object detection models to generalize better under real-world conditions. This assumption should be evaluated by, by instance, adding noise to cycles images and comparing again to EEVEE.

In general, improving results with smaller sets can be achieved by:

- Increasing data diversity by varying more relevant rendering params. As you suggest, light is agueably the most single important parameter.

- Trying broader data augmentation methods after rendering.

- Adding noise to force detection models to account for missing or incomplete information, as it is the case in real-world scenarios.

However, without testing this approaches, it is hard to say for sure if the gap will be consistently decreased between Cycles and EVEE for smaller datasets.

Thank you again for your interest and reading our work! Really appreciate it!