The field of autonomous systems is witnessing significant advancements in synthetic data generation, enabling more robust and generalizable models. Researchers are focusing on creating high-fidelity datasets that can mimic real-world scenarios, allowing for better training and evaluation of autonomous systems. This includes the development of datasets for stereo matching, end-to-end autonomous driving, and medical robotics, which are crucial for improving depth perception, safety, and accuracy. Noteworthy papers in this area include: StereoCarla, which presents a high-fidelity synthetic stereo dataset for autonomous driving scenarios, demonstrating improved generalization accuracy across multiple benchmarks. TeraSim-World, which introduces an automated pipeline for synthesizing realistic and geographically diverse safety-critical data for end-to-end autonomous driving. ROOM, which proposes a comprehensive simulation framework for generating photorealistic medical datasets for continuum robot training. Sea-ing Through Scattered Rays, which revisits the image formation model for realistic underwater image generation, including the commonly omitted forward scattering term.