title: Compact 3D Gaussian Splatting for Static and Dynamic Radiance Fields
summary: 3D Gaussian splatting (3DGS) has recently emerged as an alternative
  representation that leverages a 3D Gaussian-based representation and introduces
  an approximated volumetric rendering, achieving very fast rendering speed and
  promising image quality. Furthermore, subsequent studies have successfully
  extended 3DGS to dynamic 3D scenes, demonstrating its wide range of
  applications. However, a significant drawback arises as 3DGS and its following
  methods entail a substantial number of Gaussians to maintain the high fidelity
  of the rendered images, which requires a large amount of memory and storage. To
  address this critical issue, we place a specific emphasis on two key
  objectives: reducing the number of Gaussian points without sacrificing
  performance and compressing the Gaussian attributes, such as view-dependent
  color and covariance. To this end, we propose a learnable mask strategy that
  significantly reduces the number of Gaussians while preserving high
  performance. In addition, we propose a compact but effective representation of
  view-dependent color by employing a grid-based neural field rather than relying
  on spherical harmonics. Finally, we learn codebooks to compactly represent the
  geometric and temporal attributes by residual vector quantization. With model
  compression techniques such as quantization and entropy coding, we consistently
  show over 25x reduced storage and enhanced rendering speed compared to 3DGS for
  static scenes, while maintaining the quality of the scene representation. For
  dynamic scenes, our approach achieves more than 12x storage efficiency and
  retains a high-quality reconstruction compared to the existing state-of-the-art
  methods. Our work provides a comprehensive framework for 3D scene
  representation, achieving high performance, fast training, compactness, and
  real-time rendering. Our project page is available at
  https://maincold2.github.io/c3dgs/.
paper_authors._id: 66b428038103b78054427b22
paper_authors.name: Joo Chan Lee
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-08-08T10:56:12.094Z
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: 66b428038103b78054427b23
paper_authors.name: Daniel Rho
paper_authors._id: 66b428038103b78054427b24
paper_authors.name: Xiangyu Sun
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-08-08T10:56:31.161Z
paper_authors._id: 634d10d4a252e0c53f99534c
paper_authors.avatarUrl: /avatars/d8a486e105769ffee59cf996a4f51686.svg
paper_authors.fullname: Sun Xiang yu
paper_authors.type: user
paper_authors.user: xysun
paper_authors._id: 66b428038103b78054427b25
paper_authors.name: Jong Hwan Ko
paper_authors._id: 66b428038103b78054427b26
paper_authors.name: Eunbyung Park
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-08-08T10:56:44.688Z
paper_authors._id: 655e0141d36a195f663ee4b0
paper_authors.avatarUrl: /avatars/64609d6fd1581e6cf3e057ee569d69a1.svg
paper_authors.fullname: Eunbyung Park
paper_authors.type: user
paper_authors.user: epark
paper_title: Compact 3D Gaussian Splatting for Static and Dynamic Radiance Fields
paper_summary: 3D Gaussian splatting (3DGS) has recently emerged as an alternative
  representation that leverages a 3D Gaussian-based representation and introduces
  an approximated volumetric rendering, achieving very fast rendering speed and
  promising image quality. Furthermore, subsequent studies have successfully
  extended 3DGS to dynamic 3D scenes, demonstrating its wide range of
  applications. However, a significant drawback arises as 3DGS and its following
  methods entail a substantial number of Gaussians to maintain the high fidelity
  of the rendered images, which requires a large amount of memory and storage. To
  address this critical issue, we place a specific emphasis on two key
  objectives: reducing the number of Gaussian points without sacrificing
  performance and compressing the Gaussian attributes, such as view-dependent
  color and covariance. To this end, we propose a learnable mask strategy that
  significantly reduces the number of Gaussians while preserving high
  performance. In addition, we propose a compact but effective representation of
  view-dependent color by employing a grid-based neural field rather than relying
  on spherical harmonics. Finally, we learn codebooks to compactly represent the
  geometric and temporal attributes by residual vector quantization. With model
  compression techniques such as quantization and entropy coding, we consistently
  show over 25x reduced storage and enhanced rendering speed compared to 3DGS for
  static scenes, while maintaining the quality of the scene representation. For
  dynamic scenes, our approach achieves more than 12x storage efficiency and
  retains a high-quality reconstruction compared to the existing state-of-the-art
  methods. Our work provides a comprehensive framework for 3D scene
  representation, achieving high performance, fast training, compactness, and
  real-time rendering. Our project page is available at
  https://maincold2.github.io/c3dgs/.
paper_ai_keywords: 3D Gaussian-based representation volumetric rendering Gaussian points Gaussian attributes view-dependent color covariance learnable mask strategy grid-based neural field spherical harmonics codebooks residual vector quantization quantization entropy coding 3D scene representation static scenes dynamic scenes high-quality reconstruction real-time rendering