title: Sequence Matters: Harnessing Video Models in 3D Super-Resolution
summary: 3D super-resolution aims to reconstruct high-fidelity 3D models from
  low-resolution (LR) multi-view images. Early studies primarily focused on
  single-image super-resolution (SISR) models to upsample LR images into
  high-resolution images. However, these methods often lack view consistency
  because they operate independently on each image. Although various
  post-processing techniques have been extensively explored to mitigate these
  inconsistencies, they have yet to fully resolve the issues. In this paper, we
  perform a comprehensive study of 3D super-resolution by leveraging video
  super-resolution (VSR) models. By utilizing VSR models, we ensure a higher
  degree of spatial consistency and can reference surrounding spatial
  information, leading to more accurate and detailed reconstructions. Our
  findings reveal that VSR models can perform remarkably well even on sequences
  that lack precise spatial alignment. Given this observation, we propose a
  simple yet practical approach to align LR images without involving fine-tuning
  or generating 'smooth' trajectory from the trained 3D models over LR images.
  The experimental results show that the surprisingly simple algorithms can
  achieve the state-of-the-art results of 3D super-resolution tasks on standard
  benchmark datasets, such as the NeRF-synthetic and MipNeRF-360 datasets.
  Project page: https://ko-lani.github.io/Sequence-Matters
paper_authors._id: 676687cb5b17ac358c9ff22b
paper_authors.name: Hyun-kyu Ko
paper_authors.status: claimed_verified
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paper_authors.fullname: Lani Ko
paper_authors.type: user
paper_authors.user: lanikoisgod
paper_authors._id: 676687cb5b17ac358c9ff22c
paper_authors.name: Dongheok Park
paper_authors.status: claimed_verified
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paper_authors.fullname: Dongheok Park
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paper_authors.user: HEOK
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paper_authors.name: Youngin Park
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paper_authors.fullname: Youngin Park
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paper_authors.user: yi0109-park
paper_authors._id: 676687cb5b17ac358c9ff22e
paper_authors.name: Byeonghyeon Lee
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paper_authors.fullname: Byeonghyeon Lee
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paper_authors.user: blee
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paper_authors.name: Juhee Han
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paper_authors._id: 66036d0530394deb4ae1defd
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paper_authors.fullname: Juhee Han
paper_authors.type: user
paper_authors.user: juxhee
paper_authors._id: 676687cb5b17ac358c9ff230
paper_authors.name: Eunbyung Park
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-12-23T11:35:02.518Z
paper_authors._id: 655e0141d36a195f663ee4b0
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paper_authors.fullname: Eunbyung Park
paper_authors.type: user
paper_authors.user: epark
paper_title: Sequence Matters: Harnessing Video Models in 3D Super-Resolution
paper_summary: 3D super-resolution aims to reconstruct high-fidelity 3D models from
  low-resolution (LR) multi-view images. Early studies primarily focused on
  single-image super-resolution (SISR) models to upsample LR images into
  high-resolution images. However, these methods often lack view consistency
  because they operate independently on each image. Although various
  post-processing techniques have been extensively explored to mitigate these
  inconsistencies, they have yet to fully resolve the issues. In this paper, we
  perform a comprehensive study of 3D super-resolution by leveraging video
  super-resolution (VSR) models. By utilizing VSR models, we ensure a higher
  degree of spatial consistency and can reference surrounding spatial
  information, leading to more accurate and detailed reconstructions. Our
  findings reveal that VSR models can perform remarkably well even on sequences
  that lack precise spatial alignment. Given this observation, we propose a
  simple yet practical approach to align LR images without involving fine-tuning
  or generating 'smooth' trajectory from the trained 3D models over LR images.
  The experimental results show that the surprisingly simple algorithms can
  achieve the state-of-the-art results of 3D super-resolution tasks on standard
  benchmark datasets, such as the NeRF-synthetic and MipNeRF-360 datasets.
  Project page: https://ko-lani.github.io/Sequence-Matters
paper_ai_summary: Video super-resolution models enhance 3D super-resolution by ensuring spatial consistency across low-resolution multi-view images without the need for complex alignment techniques.
paper_ai_keywords: single-image super-resolution view consistency video super-resolution spatial consistency NeRF-synthetic MipNeRF-360