title: Optimized Minimal 3D Gaussian Splatting
summary: 3D Gaussian Splatting (3DGS) has emerged as a powerful representation for
  real-time, high-performance rendering, enabling a wide range of applications.
  However, representing 3D scenes with numerous explicit Gaussian primitives
  imposes significant storage and memory overhead. Recent studies have shown that
  high-quality rendering can be achieved with a substantially reduced number of
  Gaussians when represented with high-precision attributes. Nevertheless,
  existing 3DGS compression methods still rely on a relatively large number of
  Gaussians, focusing primarily on attribute compression. This is because a
  smaller set of Gaussians becomes increasingly sensitive to lossy attribute
  compression, leading to severe quality degradation. Since the number of
  Gaussians is directly tied to computational costs, it is essential to reduce
  the number of Gaussians effectively rather than only optimizing storage. In
  this paper, we propose Optimized Minimal Gaussians representation (OMG), which
  significantly reduces storage while using a minimal number of primitives.
  First, we determine the distinct Gaussian from the near ones, minimizing
  redundancy without sacrificing quality. Second, we propose a compact and
  precise attribute representation that efficiently captures both continuity and
  irregularity among primitives. Additionally, we propose a sub-vector
  quantization technique for improved irregularity representation, maintaining
  fast training with a negligible codebook size. Extensive experiments
  demonstrate that OMG reduces storage requirements by nearly 50% compared to the
  previous state-of-the-art and enables 600+ FPS rendering while maintaining high
  rendering quality. Our source code is available at
  https://maincold2.github.io/omg/.
paper_authors._id: 67e230f384513315a91c5602
paper_authors.name: Joo Chan Lee
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-25T08:20:07.628Z
paper_authors._id: 664207e5af62c6c26653b369
paper_authors.avatarUrl: /avatars/031453340e114efb10b7d650e1d1c384.svg
paper_authors.fullname: Joo Chan Lee
paper_authors.type: user
paper_authors.user: maincold2
paper_authors._id: 67e230f384513315a91c5603
paper_authors.name: Jong Hwan Ko
paper_authors._id: 67e230f384513315a91c5604
paper_authors.name: Eunbyung Park
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-25T09:25:09.995Z
paper_authors._id: 655e0141d36a195f663ee4b0
paper_authors.avatarUrl: /avatars/97bb695ccefdcb2139b94bcae808cf99.svg
paper_authors.fullname: Eunbyung Park
paper_authors.type: user
paper_authors.user: epark
paper_title: Optimized Minimal 3D Gaussian Splatting
paper_summary: 3D Gaussian Splatting (3DGS) has emerged as a powerful representation for
  real-time, high-performance rendering, enabling a wide range of applications.
  However, representing 3D scenes with numerous explicit Gaussian primitives
  imposes significant storage and memory overhead. Recent studies have shown that
  high-quality rendering can be achieved with a substantially reduced number of
  Gaussians when represented with high-precision attributes. Nevertheless,
  existing 3DGS compression methods still rely on a relatively large number of
  Gaussians, focusing primarily on attribute compression. This is because a
  smaller set of Gaussians becomes increasingly sensitive to lossy attribute
  compression, leading to severe quality degradation. Since the number of
  Gaussians is directly tied to computational costs, it is essential to reduce
  the number of Gaussians effectively rather than only optimizing storage. In
  this paper, we propose Optimized Minimal Gaussians representation (OMG), which
  significantly reduces storage while using a minimal number of primitives.
  First, we determine the distinct Gaussian from the near ones, minimizing
  redundancy without sacrificing quality. Second, we propose a compact and
  precise attribute representation that efficiently captures both continuity and
  irregularity among primitives. Additionally, we propose a sub-vector
  quantization technique for improved irregularity representation, maintaining
  fast training with a negligible codebook size. Extensive experiments
  demonstrate that OMG reduces storage requirements by nearly 50% compared to the
  previous state-of-the-art and enables 600+ FPS rendering while maintaining high
  rendering quality. Our source code is available at
  https://maincold2.github.io/omg/.
paper_ai_summary: OMG reduces storage and computational costs in 3D Gaussian Splatting by minimizing Gaussian primitives and optimizing attribute representation, while maintaining high rendering quality.
paper_ai_keywords: Gaussian primitives 3D Gaussian Splatting attribute compression distinct Gaussian sub-vector quantization high-precision attributes