Metric-aware processing of spherical imagery

Processing spherical images is challenging. Because no spherical parameterization is globally uniform, an accurate solver must account for the spatially varying metric. We present the first efficient metric-aware solver for Laplacian processing of spherical data. Our approach builds on the commonly used equirectangular parameterization, which provides differentiability, axial symmetry, and grid sampling. Crucially, axial symmetry lets us discretize the Laplacian operator just once per grid row. One difficulty is that anisotropy near the poles leads to a poorly conditioned system. Our solution is to construct an adapted hierarchy of finite elements, adjusted at the poles to maintain derivative continuity, and selectively coarsened to bound element anisotropy. The resulting elements are nested both within and across resolution levels. A streaming multigrid solver over this hierarchy achieves excellent convergence rate and scales to huge images. We demonstrate applications in reaction-diffusion texture synthesis and panorama stitching and sharpening.