RainGAN-Kathmandu: A Generative Adversarial Framework for Synthetic Rainfall Augmentation in Urban Road Scene Datasets
Published in IEEE TENCON 2025, Malaysia, 2025
A generative adversarial framework for adding realistic synthetic rainfall to urban road-scene imagery, enabling more robust perception models under adverse weather. Accepted for presentation at IEEE TENCON 2025, Malaysia (Oct 27–30, 2025).
Recommended citation: N. K. Shah, J. Gadde, N. Singh, C. P. Maurya, and S. K. Singh. (2025). "RainGAN-Kathmandu: A Generative Adversarial Framework for Synthetic Rainfall Augmentation in Urban Road Scene Datasets." IEEE TENCON 2025, Malaysia.
