title: GES: Generalized Exponential Splatting for Efficient Radiance Field
    Rendering
summary: 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 .
paper_authors._id: 65cee8c2d8b82d378f76c761
paper_authors.name: Abdullah Hamdi
paper_authors.status: admin_assigned
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paper_authors.fullname: Abdullah Hamdi
paper_authors.type: user
paper_authors.user: ajhamdi
paper_authors._id: 65cee8c2d8b82d378f76c762
paper_authors.name: Luke Melas-Kyriazi
paper_authors.status: admin_assigned
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paper_authors.fullname: Luke Melas-Kyriazi
paper_authors.type: user
paper_authors.user: lukemelas
paper_authors._id: 65cee8c2d8b82d378f76c763
paper_authors.name: Guocheng Qian
paper_authors.status: admin_assigned
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paper_authors.fullname: Guocheng Qian
paper_authors.type: user
paper_authors.user: guochengqian
paper_authors._id: 65cee8c2d8b82d378f76c764
paper_authors.name: Jinjie Mai
paper_authors._id: 65cee8c2d8b82d378f76c765
paper_authors.name: Ruoshi Liu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-02-16T14:16:04.682Z
paper_authors._id: 63408322ea76a1e0b47b50e8
paper_authors.avatarUrl: /avatars/8e2453313006fdf70b99bc7d29c738b1.svg
paper_authors.fullname: Ruoshi Liu
paper_authors.type: user
paper_authors.user: rliu
paper_authors._id: 65cee8c2d8b82d378f76c766
paper_authors.name: Carl Vondrick
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-02-16T14:16:10.824Z
paper_authors._id: 63a4bfcbcde2b28f82a2c50c
paper_authors.avatarUrl: /avatars/9d978a6f0caf6ad92ec4138a84a39e53.svg
paper_authors.fullname: Carl Vondrick
paper_authors.type: user
paper_authors.user: cvondrick
paper_authors._id: 65cee8c2d8b82d378f76c767
paper_authors.name: Bernard Ghanem
paper_authors._id: 65cee8c2d8b82d378f76c768
paper_authors.name: Andrea Vedaldi
paper_title: GES: Generalized Exponential Splatting for Efficient Radiance Field
    Rendering
paper_summary: 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 .
paper_ai_keywords: Generalized Exponential Splatting (GES) Generalized Exponential Function (GEF) 3D Gaussian Splatting frequency-modulated loss novel-view synthesis