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        content="S2Gaussian: Sparse-View Super-Resolution 3D Gaussian Splatting">
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  <title>S2Gaussian: Sparse-View Super-Resolution 3D Gaussian Splatting</title>

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          <h1 class="title is-1 publication-title">S2Gaussian: Sparse-View Super-Resolution 3D Gaussian Splatting</h1>
          <h1 class="title is-3 publication-title">CVPR 2025</h1>
          <div class="is-size-5 publication-authors">
            <span class="author-block">
              <a href="https://scholar.google.com/citations?hl=zh-CN&user=ld5UUg0AAAAJ">Yecong Wan</a><sup>1</sup>,</span>
            <span class="author-block">
              <a href="https://scholar.google.com/citations?user=b8l5YaAAAAAJ&hl=zh-CN">Mingwen Shao</a><sup>1</sup>,</span>
            <span class="author-block">
              <a href="https://scholar.google.com/citations?user=0UPAuk0AAAAJ&hl=zh-CN&oi=ao">Yuanshuo Cheng</a><sup>1</sup>,
            </span>
            <span class="author-block">
              <a href="https://scholar.google.com/citations?user=rUOpCEYAAAAJ&hl=zh-CN">Wangmeng Zuo</a><sup>2</sup>,
            </span>
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          <div class="is-size-5 publication-authors">
            <span class="author-block"><sup>1</sup>China University of Petroleum (East China)</span>
            <span class="author-block"><sup>2</sup>Harbin Institute of Technology</span>
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        <h2 class="title is-3">Abstract</h2>
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          <p>
           In this paper, we aim ambitiously for a realistic yet challenging problem, namely, how to reconstruct high-quality 3D scenes from sparse low-resolution views that simultaneously suffer from deficient perspectives and clarity. Whereas existing methods only deal with either sparse views or low-resolution observations, they fail to handle such hybrid and complicated scenarios. To this end, we propose a novel Sparse-view Super-resolution 3D Gaussian Splatting framework, dubbed S2Gaussian, that can reconstruct structure-accurate and detail-faithful 3D scenes with only sparse and low-resolution views. The S2Gaussian operates in a two-stage fashion. In the first stage, we initially optimize a low-resolution Gaussian representation with depth regularization and densify it to initialize the high-resolution Gaussians through a tailored Gaussian Shuffle Split operation. In the second stage, we refine the high-resolution Gaussians with the super-resolved images generated from both original sparse views and pseudo-views rendered by the low-resolution Gaussians. In which a customized blur-free inconsistency modeling scheme and a 3D robust optimization strategy are elaborately designed to mitigate multi-view inconsistency and eliminate erroneous updates caused by imperfect supervision. Extensive experiments demonstrate superior results and in particular establishing new state-of-the-art performances with more consistent geometry and finer details.
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          <h2 class="title is-3">Method Overview</h2>
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            <p>
              The S2Gaussian initially optimizes an LR GS and densifies it to initialize the HR GS through a tailored Gaussian Shuffle Split operation. Then, the original sparse views along with the pseudo views rendered by the LR GS are super-resolved together to refine the high-resolution texture with 3D robust optimization. In which an inconsistency modeling module (IM) and a blur proposal module are incorporated to mitigate inconsistency and blurriness, aiming to create 3D scenes with high-fidelity texture details.
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          <h2 class="title is-3">Visual Results</h2>
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          <h2 class="title is-3">Visual Results</h2>
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          <h2 class="title is-3">Visual Results</h2>
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    <h2 class="title">BibTeX</h2>
    <pre><code>@article{wan2025s2gaussian,
  author    = {Yecong Wan and Yuanshuo Cheng and Mingwen Shao and Wangmeng Zuo},
  title     = {S2Gaussian: Sparse-View Super-Resolution 3D Gaussian Splatting},
  journal   = {CVPR},
  year      = {2025},
}</code></pre>
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