GES: Generalized Exponential Splatting for Efficient Radiance Field
  Rendering
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 https://abdullahamdi.com/ges .
65cee8c2d8b82d378f76c761
Abdullah Hamdi
admin_assigned
2024-02-16T14:15:33.855Z
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Abdullah Hamdi
user
ajhamdi
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Luke Melas-Kyriazi
admin_assigned
2024-02-16T14:15:41.867Z
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Luke Melas-Kyriazi
user
lukemelas
65cee8c2d8b82d378f76c763
Guocheng Qian
admin_assigned
2024-02-16T14:15:48.079Z
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Guocheng Qian
user
guochengqian
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Jinjie Mai
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Ruoshi Liu
admin_assigned
2024-02-16T14:16:04.682Z
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Ruoshi Liu
user
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Carl Vondrick
admin_assigned
2024-02-16T14:16:10.824Z
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Carl Vondrick
user
cvondrick
65cee8c2d8b82d378f76c767
Bernard Ghanem
65cee8c2d8b82d378f76c768
Andrea Vedaldi
GES: Generalized Exponential Splatting for Efficient Radiance Field
  Rendering
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 https://abdullahamdi.com/ges .
GES, a new representation using Generalized Exponential Function, outperforms Gaussian Splatting in 3D reconstruction and generation by requiring fewer particles and improving memory efficiency and rendering speed.
3D Gaussian Splatting GES Generalized Exponential Splatting Generalized Exponential Function (GEF) natural-occurring signals frequency-modulated loss novel-view synthesis