Summary
In this chapter, we introduced a well-known method for synthetic data generation based on simulators and rendering engines. We learned how to generate synthetic data. We highlighted the main challenges and we discussed AirSim and CARLA simulators as examples of this data generation approach. We have seen that by using simulators and game engines, we can generate large-scale, rich, and automatically annotated synthetic data for many applications. It reduces the cost and effort and provides an ideal solution for training robust ML models.
In the next chapter, we will learn about a new method for synthetic data generation using Generative Adversarial Networks (GANs).
