Spherical Representation

Spherical representation is a technique used to model data residing on the surface of a sphere, addressing challenges inherent in representing and processing data with inherent circular or spherical structures, such as 360° images or molecular geometries. Current research focuses on developing novel algorithms, including graph convolutional networks and spherical convolutions, to efficiently extract features and perform tasks like depth estimation, object pose estimation, and classification from spherical data. This approach improves accuracy and efficiency in various applications, ranging from image processing and computer vision to scientific discovery in fields like materials science and biology, by leveraging the inherent geometric properties of the data.