Large-scale synthetic data is needed to support the deep learning big-bang that started in the recent decade and influenced almost all scientific fields. Most of the synthetic data generation solutions are task-specific or unscalable while the others are expensive, based on commercial games, or unreliable. In this work, a new rendering engine called Silver is presented in detail. Photo-realism, diversity, scalability, and full 3D virtual world generation at run-time are the key aspects of this work. The photo-realism was approached by utilizing the state-of-the-art High Definition Render Pipeline (HDRP) of the Unity game engine. In parallel, the Procedural Content Generation (PCG) concept was employed to create a full 3D virtual world at run-time, while the scalability of the system was attained by taking advantage of the modular approach followed as we built the system from scratch. Silver can be used to provide clean, unbiased, and large-scale training and testing data for various computer vision tasks
