Spherical Signal

Spherical signal processing focuses on analyzing and manipulating data defined on the surface of a sphere, a common structure in many scientific domains. Current research emphasizes developing efficient and accurate neural network architectures, such as spherical convolutional neural networks (S-CNNs) and transformers adapted for spherical geometry, to handle the unique challenges posed by spherical data, including distortions near poles and varying resolutions. These advancements are improving the analysis of diverse spherical signals, including weather patterns, cosmic microwave background radiation, and depth maps, leading to better data representation, feature extraction, and ultimately, more accurate scientific insights and improved applications in various fields.