Synthetic RGB

Synthetic RGB data generation is a rapidly growing field focused on creating realistic simulated images for training and evaluating computer vision models, addressing the limitations of real-world data acquisition. Current research emphasizes generating diverse and high-fidelity synthetic datasets using techniques like domain randomization, NeRFs, and diffusion models, often coupled with advanced architectures such as vision transformers and graph neural networks for improved performance. This work is crucial for advancing various applications, including autonomous driving, robotics, and 3D scene understanding, by providing large-scale, annotated datasets for training robust and generalizable models that overcome the "reality gap" between synthetic and real-world data.