title: RayGauss: Volumetric Gaussian-Based Ray Casting for Photorealistic Novel
    View Synthesis
summary: Differentiable volumetric rendering-based methods made significant progress
  in novel view synthesis. On one hand, innovative methods have replaced the
  Neural Radiance Fields (NeRF) network with locally parameterized structures,
  enabling high-quality renderings in a reasonable time. On the other hand,
  approaches have used differentiable splatting instead of NeRF's ray casting to
  optimize radiance fields rapidly using Gaussian kernels, allowing for fine
  adaptation to the scene. However, differentiable ray casting of irregularly
  spaced kernels has been scarcely explored, while splatting, despite enabling
  fast rendering times, is susceptible to clearly visible artifacts.
    Our work closes this gap by providing a physically consistent formulation of
  the emitted radiance c and density {\sigma}, decomposed with Gaussian functions
  associated with Spherical Gaussians/Harmonics for all-frequency colorimetric
  representation. We also introduce a method enabling differentiable ray casting
  of irregularly distributed Gaussians using an algorithm that integrates
  radiance fields slab by slab and leverages a BVH structure. This allows our
  approach to finely adapt to the scene while avoiding splatting artifacts. As a
  result, we achieve superior rendering quality compared to the state-of-the-art
  while maintaining reasonable training times and achieving inference speeds of
  25 FPS on the Blender dataset. Project page with videos and code:
  https://raygauss.github.io/
paper_authors._id: 66b422ea3a88f464e26a50b9
paper_authors.name: Hugo Blanc
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-08-08T13:08:17.757Z
paper_authors._id: 66b4c094aa581aef1f61199b
paper_authors.avatarUrl: /avatars/ea0665b19091cb84d644da088b13d294.svg
paper_authors.fullname: Hugo Blanc
paper_authors.type: user
paper_authors.user: hublbl
paper_authors._id: 66b422ea3a88f464e26a50ba
paper_authors.name: Jean-Emmanuel Deschaud
paper_authors._id: 66b422ea3a88f464e26a50bb
paper_authors.name: Alexis Paljic
paper_title: RayGauss: Volumetric Gaussian-Based Ray Casting for Photorealistic Novel
    View Synthesis
paper_summary: Differentiable volumetric rendering-based methods made significant progress
  in novel view synthesis. On one hand, innovative methods have replaced the
  Neural Radiance Fields (NeRF) network with locally parameterized structures,
  enabling high-quality renderings in a reasonable time. On the other hand,
  approaches have used differentiable splatting instead of NeRF's ray casting to
  optimize radiance fields rapidly using Gaussian kernels, allowing for fine
  adaptation to the scene. However, differentiable ray casting of irregularly
  spaced kernels has been scarcely explored, while splatting, despite enabling
  fast rendering times, is susceptible to clearly visible artifacts.
    Our work closes this gap by providing a physically consistent formulation of
  the emitted radiance c and density {\sigma}, decomposed with Gaussian functions
  associated with Spherical Gaussians/Harmonics for all-frequency colorimetric
  representation. We also introduce a method enabling differentiable ray casting
  of irregularly distributed Gaussians using an algorithm that integrates
  radiance fields slab by slab and leverages a BVH structure. This allows our
  approach to finely adapt to the scene while avoiding splatting artifacts. As a
  result, we achieve superior rendering quality compared to the state-of-the-art
  while maintaining reasonable training times and achieving inference speeds of
  25 FPS on the Blender dataset. Project page with videos and code:
  https://raygauss.github.io/
paper_ai_summary: The work introduces a physically consistent volumetric rendering method using differentiable ray casting of irregularly distributed Gaussians, achieving high-quality rendering with fast inference.
paper_ai_keywords: Neural Radiance Fields (NeRF) locally parameterized structures differentiable splatting Gaussian kernels differentiable ray casting Gaussian functions Spherical Gaussians/Harmonics BVH structure rendering quality Blender dataset