In the context of the continuously increasing complexity of assistance and automation functions used in vehicles, traditional testing and design methods are reaching their limits. The complexity and variability of real-world driving scenarios make it challenging to test and validate these functions accurately and efficiently using conventional approaches. Therefore, virtual techniques, simulation- based methods, and scenario-based validation have become crucial in the modern development process. One of the key challenges in virtual testing is the generation or derivation of high-fidelity and photorealistic virtual environment data. While existing methods for creating virtual environments through measurement campaigns capture most of the relevant aspects of the real world, they often struggle with highly manual processes, associated costs, and limited validity for specific environmental situations. This limitation is significant because real assistance systems must be able to ensure safe driving in any situation. We present procedures and methods that offer an innovative approach to generate and derive high-fidelity and photorealistic virtual environment data for virtual testing and validation of advanced driver assistance systems and autonomous driving functions in vehicles. By synthesizing available data sources and applying machine learning techniques, this approach overcomes the limitations of traditional methods and provides a more realistic and efficient solution.