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.

本文研究球面数据的混合神经表示方法，特别关注天气和气候数据以及宇宙微波背景数据。我们提出了一种名为 HNeR-S 的新方法，通过使用球面特征网格和多层感知器结合来预测目标信号，经过广泛验证在回归、超分辨率、时间插值和压缩任务方面具有有效性。