Advancements in 3D Gaussian Splatting have significantly accelerated 3D
reconstruction and generation. However, it may require a large number of
Gaussians, which creates a substantial memory footprint. This paper introduces
GES (Generalized Exponential Splatting), a novel representation that employs
Generalized Exponential Function (GEF) to model 3D scenes, requiring far fewer
particles to represent a scene and thus significantly outperforming Gaussian
Splatting methods in efficiency with a plug-and-play replacement ability for
Gaussian-based utilities. GES is validated theoretically and empirically in
both principled 1D setup and realistic 3D scenes.
It is shown to represent signals with sharp edges more accurately, which are
typically challenging for Gaussians due to their inherent low-pass
characteristics. Our empirical analysis demonstrates that GEF outperforms
Gaussians in fitting natural-occurring signals (e.g. squares, triangles, and
parabolic signals), thereby reducing the need for extensive splitting
operations that increase the memory footprint of Gaussian Splatting. With the
aid of a frequency-modulated loss, GES achieves competitive performance in
novel-view synthesis benchmarks while requiring less than half the memory
storage of Gaussian Splatting and increasing the rendering speed by up to 39%.
The code is available on the project website this https URL .

通过引入 Generalized Exponential Splatting (GES) 方法，使用 Generalized Exponential Function (GEF) 模型对 3D 场景进行建模，减少了内存占用并显著提高了效率。