Visibility Estimation

Visibility estimation focuses on determining what is observable from a given perspective, a crucial task across diverse fields like autonomous navigation, robotics, and meteorology. Current research emphasizes developing efficient algorithms, often employing neural networks (e.g., multi-layer perceptrons, graph convolutional networks) and incorporating physical models (e.g., Koschmieder's law) to improve accuracy and speed, particularly in challenging conditions like fog or snow. These advancements are significant for enhancing safety and performance in autonomous systems, improving weather forecasting accuracy, and enabling more robust perception in complex environments. The development of standardized metrics for evaluating visibility estimation methods is also a growing area of focus.