Hybrid Neural Representations for Spherical Data
Hyomin Kim Yunhui Jang Jaeho Lee Sungsoo Ahn
In this paper, we study hybrid neural representations for spherical data, a
domain of increasing relevance in scientific research. In particular, our work
focuses on weather and climate data as well as comic microwave background (CMB)
data. Although previous studies have delved into coordinate-based neural
representations for spherical signals, they often fail to capture the intricate
details of highly nonlinear signals. To address this limitation, we introduce a
novel approach named Hybrid Neural Representations for Spherical data (HNeR-S).
Our main idea is to use spherical feature-grids to obtain positional features
which are combined with a multilayer perception to predict the target signal.
We consider feature-grids with equirectangular and hierarchical equal area
isolatitude pixelization structures that align with weather data and CMB data,
respectively. We extensively verify the effectiveness of our HNeR-S for
regression, super-resolution, temporal interpolation, and compression tasks.