Spherical Convolutional Neural Network

Spherical convolutional neural networks (Spherical CNNs) extend traditional CNNs to process data residing on the surface of a sphere, addressing limitations of planar projections when dealing with inherently spherical data like 360° images or 3D point clouds. Current research focuses on developing efficient and equivariant Spherical CNN architectures, including those based on spherical harmonics, icosahedral representations, and novel convolution operations like Möbius convolutions, to improve accuracy and computational efficiency in various applications. These advancements are significantly impacting fields such as medical imaging, computer vision, and audio processing by enabling more accurate and robust analysis of spherical data, leading to improved performance in tasks like object pose estimation, semantic segmentation, and 3D shape generation.