title: GaussianSR: 3D Gaussian Super-Resolution with 2D Diffusion Priors
summary: Achieving high-resolution novel view synthesis (HRNVS) from low-resolution
  input views is a challenging task due to the lack of high-resolution data.
  Previous methods optimize high-resolution Neural Radiance Field (NeRF) from
  low-resolution input views but suffer from slow rendering speed. In this work,
  we base our method on 3D Gaussian Splatting (3DGS) due to its capability of
  producing high-quality images at a faster rendering speed. To alleviate the
  shortage of data for higher-resolution synthesis, we propose to leverage
  off-the-shelf 2D diffusion priors by distilling the 2D knowledge into 3D with
  Score Distillation Sampling (SDS). Nevertheless, applying SDS directly to
  Gaussian-based 3D super-resolution leads to undesirable and redundant 3D
  Gaussian primitives, due to the randomness brought by generative priors. To
  mitigate this issue, we introduce two simple yet effective techniques to reduce
  stochastic disturbances introduced by SDS. Specifically, we 1) shrink the range
  of diffusion timestep in SDS with an annealing strategy; 2) randomly discard
  redundant Gaussian primitives during densification. Extensive experiments have
  demonstrated that our proposed GaussainSR can attain high-quality results for
  HRNVS with only low-resolution inputs on both synthetic and real-world
  datasets. Project page: https://chchnii.github.io/GaussianSR/
paper_authors._id: 666fe9a81f975c1f86db1d51
paper_authors.name: Xiqian Yu
paper_authors.status: extracted_confirmed
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paper_authors.fullname: Tianyu He
paper_authors.type: user
paper_authors.user: deeptimhe
paper_authors._id: 666fe9a81f975c1f86db1d52
paper_authors.name: Hanxin Zhu
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paper_authors.avatarUrl: /avatars/384b35ea59b55f8d454be9efb7cce47c.svg
paper_authors.fullname: Hanxin ZHU
paper_authors.type: user
paper_authors.user: ingzzzz
paper_authors._id: 666fe9a81f975c1f86db1d53
paper_authors.name: Tianyu He
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-06-17T16:20:47.708Z
paper_authors._id: 619b7b1cab4c7b7f16a7d59e
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paper_authors.fullname: Tianyu He
paper_authors.type: user
paper_authors.user: deeptimhe
paper_authors._id: 666fe9a81f975c1f86db1d54
paper_authors.name: Zhibo Chen
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-06-18T07:18:59.227Z
paper_authors._id: 666fe497fb4f22815598fb77
paper_authors.avatarUrl: /avatars/dc61b962c11b916b750b017fe8010493.svg
paper_authors.fullname: Xiqian Yu
paper_authors.type: user
paper_authors.user: chchnii
paper_title: GaussianSR: 3D Gaussian Super-Resolution with 2D Diffusion Priors
paper_summary: Achieving high-resolution novel view synthesis (HRNVS) from low-resolution
  input views is a challenging task due to the lack of high-resolution data.
  Previous methods optimize high-resolution Neural Radiance Field (NeRF) from
  low-resolution input views but suffer from slow rendering speed. In this work,
  we base our method on 3D Gaussian Splatting (3DGS) due to its capability of
  producing high-quality images at a faster rendering speed. To alleviate the
  shortage of data for higher-resolution synthesis, we propose to leverage
  off-the-shelf 2D diffusion priors by distilling the 2D knowledge into 3D with
  Score Distillation Sampling (SDS). Nevertheless, applying SDS directly to
  Gaussian-based 3D super-resolution leads to undesirable and redundant 3D
  Gaussian primitives, due to the randomness brought by generative priors. To
  mitigate this issue, we introduce two simple yet effective techniques to reduce
  stochastic disturbances introduced by SDS. Specifically, we 1) shrink the range
  of diffusion timestep in SDS with an annealing strategy; 2) randomly discard
  redundant Gaussian primitives during densification. Extensive experiments have
  demonstrated that our proposed GaussainSR can attain high-quality results for
  HRNVS with only low-resolution inputs on both synthetic and real-world
  datasets. Project page: https://chchnii.github.io/GaussianSR/
paper_ai_keywords: Neural Radiance Field (NeRF) 3D Gaussian Splatting (3DGS) Score Distillation Sampling (SDS) Gaussian-based 3D super-resolution Gaussian primitives