SAGS: Structure-Aware 3D Gaussian Splatting
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/.
6631a4ec99b1fa1061c36f11
Evangelos Ververas
claimed_verified
2024-05-02T10:32:16.670Z
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Evangelos Ververas
user
eververas
6631a4ec99b1fa1061c36f12
Rolandos Alexandros Potamias
6631a4ec99b1fa1061c36f13
Jifei Song
admin_assigned
2024-05-02T08:28:15.791Z
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Jifei Song
user
seindlut
6631a4ec99b1fa1061c36f14
Jiankang Deng
admin_assigned
2024-05-02T08:28:22.593Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/1657566065867-noauth.png
JiankangDeng
user
JiankangDeng
6631a4ec99b1fa1061c36f15
Stefanos Zafeiriou
SAGS: Structure-Aware 3D Gaussian Splatting
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/.
A structure-aware Gaussian Splatting method improves rendering performance and reduces storage requirements by encoding scene geometry without compression.
NeRFs 3D Gaussian Splatting 3D-GS geometry-agnostic optimization expressivity floating points artifacts structure-aware Gaussian Splatting SAGS local-global graph representation mid-point interpolation scheme novel-view synthesis rendering quality model size depth maps