Spherical Harmonics
Overview
Spherical harmonics expand signals over directions into coefficients, making spherical data easier to analyze, transform, and compare under rotation. Common approaches include truncated harmonic expansions, spherical transforms and convolutions, SO(3)-equivariant and spin-weighted networks, and rotation-invariant descriptors. They are used for tasks ranging from Ambisonics audio and 3D appearance modeling to point-cloud registration, molecular prediction, and spatial reasoning from 360-degree views. A central tension is balancing compact, efficient low-order representations against the detail and interactions needed for high-frequency appearance, fine geometry, or chiral structures.
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