title: SAGS: Structure-Aware 3D Gaussian Splatting
summary: Following the advent of NeRFs, 3D Gaussian Splatting (3D-GS) has paved the
  way to real-time neural rendering overcoming the computational burden of
  volumetric methods. Following the pioneering work of 3D-GS, several methods
  have attempted to achieve compressible and high-fidelity performance
  alternatives. However, by employing a geometry-agnostic optimization scheme,
  these methods neglect the inherent 3D structure of the scene, thereby
  restricting the expressivity and the quality of the representation, resulting
  in various floating points and artifacts. In this work, we propose a
  structure-aware Gaussian Splatting method (SAGS) that implicitly encodes the
  geometry of the scene, which reflects to state-of-the-art rendering performance
  and reduced storage requirements on benchmark novel-view synthesis datasets.
  SAGS is founded on a local-global graph representation that facilitates the
  learning of complex scenes and enforces meaningful point displacements that
  preserve the scene's geometry. Additionally, we introduce a lightweight version
  of SAGS, using a simple yet effective mid-point interpolation scheme, which
  showcases a compact representation of the scene with up to 24times size
  reduction without the reliance on any compression strategies. Extensive
  experiments across multiple benchmark datasets demonstrate the superiority of
  SAGS compared to state-of-the-art 3D-GS methods under both rendering quality
  and model size. Besides, we demonstrate that our structure-aware method can
  effectively mitigate floating artifacts and irregular distortions of previous
  methods while obtaining precise depth maps. Project page
  https://eververas.github.io/SAGS/.
paper_authors._id: 6631a4ec99b1fa1061c36f11
paper_authors.name: Evangelos Ververas
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-05-02T10:32:16.670Z
paper_authors._id: 663369d528a7f3446313f712
paper_authors.avatarUrl: /avatars/945f10e66f604c4778eb28c78e9e7a35.svg
paper_authors.fullname: Evangelos Ververas
paper_authors.type: user
paper_authors.user: eververas
paper_authors._id: 6631a4ec99b1fa1061c36f12
paper_authors.name: Rolandos Alexandros Potamias
paper_authors._id: 6631a4ec99b1fa1061c36f13
paper_authors.name: Jifei Song
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-05-02T08:28:15.791Z
paper_authors._id: 651ef30b9ce7f94e057844fe
paper_authors.avatarUrl: /avatars/1cc60a1db525fa514e4936e1ad191ba1.svg
paper_authors.fullname: Jifei Song
paper_authors.type: user
paper_authors.user: seindlut
paper_authors._id: 6631a4ec99b1fa1061c36f14
paper_authors.name: Jiankang Deng
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-05-02T08:28:22.593Z
paper_authors._id: 62cc7a38376917c0223dd24b
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1657566065867-noauth.png
paper_authors.fullname: JiankangDeng
paper_authors.type: user
paper_authors.user: JiankangDeng
paper_authors._id: 6631a4ec99b1fa1061c36f15
paper_authors.name: Stefanos Zafeiriou
paper_title: SAGS: Structure-Aware 3D Gaussian Splatting
paper_summary: Following the advent of NeRFs, 3D Gaussian Splatting (3D-GS) has paved the
  way to real-time neural rendering overcoming the computational burden of
  volumetric methods. Following the pioneering work of 3D-GS, several methods
  have attempted to achieve compressible and high-fidelity performance
  alternatives. However, by employing a geometry-agnostic optimization scheme,
  these methods neglect the inherent 3D structure of the scene, thereby
  restricting the expressivity and the quality of the representation, resulting
  in various floating points and artifacts. In this work, we propose a
  structure-aware Gaussian Splatting method (SAGS) that implicitly encodes the
  geometry of the scene, which reflects to state-of-the-art rendering performance
  and reduced storage requirements on benchmark novel-view synthesis datasets.
  SAGS is founded on a local-global graph representation that facilitates the
  learning of complex scenes and enforces meaningful point displacements that
  preserve the scene's geometry. Additionally, we introduce a lightweight version
  of SAGS, using a simple yet effective mid-point interpolation scheme, which
  showcases a compact representation of the scene with up to 24times size
  reduction without the reliance on any compression strategies. Extensive
  experiments across multiple benchmark datasets demonstrate the superiority of
  SAGS compared to state-of-the-art 3D-GS methods under both rendering quality
  and model size. Besides, we demonstrate that our structure-aware method can
  effectively mitigate floating artifacts and irregular distortions of previous
  methods while obtaining precise depth maps. Project page
  https://eververas.github.io/SAGS/.
paper_ai_keywords: NeRFs 3D Gaussian Splatting (3D-GS) volumetric methods compressible and high-fidelity performance geometry-agnostic optimization scheme intricate points and artifacts structure-aware Gaussian Splatting method (SAGS) implicit encoding local-global graph representation mid-point interpolation scheme compact representation floating artifacts irregular distortions depth maps