title: 3D Gaussian Splatting for Real-Time Radiance Field Rendering
summary: Radiance Field methods have recently revolutionized novel-view synthesis of
  scenes captured with multiple photos or videos. However, achieving high visual
  quality still requires neural networks that are costly to train and render,
  while recent faster methods inevitably trade off speed for quality. For
  unbounded and complete scenes (rather than isolated objects) and 1080p
  resolution rendering, no current method can achieve real-time display rates. We
  introduce three key elements that allow us to achieve state-of-the-art visual
  quality while maintaining competitive training times and importantly allow
  high-quality real-time (>= 30 fps) novel-view synthesis at 1080p resolution.
  First, starting from sparse points produced during camera calibration, we
  represent the scene with 3D Gaussians that preserve desirable properties of
  continuous volumetric radiance fields for scene optimization while avoiding
  unnecessary computation in empty space; Second, we perform interleaved
  optimization/density control of the 3D Gaussians, notably optimizing
  anisotropic covariance to achieve an accurate representation of the scene;
  Third, we develop a fast visibility-aware rendering algorithm that supports
  anisotropic splatting and both accelerates training and allows realtime
  rendering. We demonstrate state-of-the-art visual quality and real-time
  rendering on several established datasets.
paper_authors._id: 64d324d461b976fdb06eec74
paper_authors.name: Bernhard Kerbl
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-02-09T13:54:23.154Z
paper_authors._id: 645578a0273f649830286cc9
paper_authors.avatarUrl: /avatars/72418cc69eab830abab2bd8fd8a397c7.svg
paper_authors.fullname: Bernhard Kerbl
paper_authors.type: user
paper_authors.user: SnoSixtyTwo
paper_authors._id: 64d324d461b976fdb06eec75
paper_authors.name: Georgios Kopanas
paper_authors._id: 64d324d461b976fdb06eec76
paper_authors.name: Thomas Leimkühler
paper_authors._id: 64d324d461b976fdb06eec77
paper_authors.name: George Drettakis
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-09-18T11:34:53.099Z
paper_authors._id: 66eaa4ba1654494d77f28d42
paper_authors.avatarUrl: /avatars/42e6d78bc6c32ea43dad0684b210c815.svg
paper_authors.fullname: George Drettakis
paper_authors.type: user
paper_authors.user: gdrett
paper_title: 3D Gaussian Splatting for Real-Time Radiance Field Rendering
paper_summary: Radiance Field methods have recently revolutionized novel-view synthesis of
  scenes captured with multiple photos or videos. However, achieving high visual
  quality still requires neural networks that are costly to train and render,
  while recent faster methods inevitably trade off speed for quality. For
  unbounded and complete scenes (rather than isolated objects) and 1080p
  resolution rendering, no current method can achieve real-time display rates. We
  introduce three key elements that allow us to achieve state-of-the-art visual
  quality while maintaining competitive training times and importantly allow
  high-quality real-time (>= 30 fps) novel-view synthesis at 1080p resolution.
  First, starting from sparse points produced during camera calibration, we
  represent the scene with 3D Gaussians that preserve desirable properties of
  continuous volumetric radiance fields for scene optimization while avoiding
  unnecessary computation in empty space; Second, we perform interleaved
  optimization/density control of the 3D Gaussians, notably optimizing
  anisotropic covariance to achieve an accurate representation of the scene;
  Third, we develop a fast visibility-aware rendering algorithm that supports
  anisotropic splatting and both accelerates training and allows realtime
  rendering. We demonstrate state-of-the-art visual quality and real-time
  rendering on several established datasets.
paper_ai_summary: A method using 3D Gaussians for scene representation and optimized rendering allows high-quality, real-time novel-view synthesis at 1080p resolution.
paper_ai_keywords: radiance field novel-view synthesis 3D Gaussians volumetric radiance fields anisotropic covariance visibility-aware rendering anisotropic splatting