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          <h1 class="title is-1 publication-title">LM-Gaussian: Boost Sparse-view 3D Gaussian Splatting with Large Model Priors</h1>
          <div class="is-size-5 publication-authors">
            <span class="author-block">
              <a href="https://hanyangyu1021.github.io/">Hanyang Yu</a><sup>1</sup>,</span>
            <span class="author-block">
              <a href="https://www.xxlong.site/">Xiaoxiao Long</a><sup>1</sup> and </span>
            <span class="author-block">
              <a href="https://ece.hkust.edu.hk/pingtan">Ping Tan</a><sup>1</sup>
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            <span class="author-block"><sup>1</sup>The Hong Kong University of Science and Technology,</span>
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     <p>The results shown are derived from <strong>3 training views</strong>.</p>
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            We aim to address sparse-view reconstruction of a 3D scene by leveraging priors from large-scale vision models.
           While recent advancements such as 3D Gaussian Splatting (3DGS) have demonstrated remarkable success in 3D reconstruction, these methods typically
            necessitate hundreds of input images that densely capture the underlying scene, making them time-consuming and impractical for real-world applications. However,
            sparse-view reconstruction is inherently ill-posed and under-constrained, often resulting in inferior and incomplete outcomes.
            This is due to issues such as failed initialization, overfitting to
            input images, and a lack of detail. To mitigate these challenges,
            we introduce LM-Gaussian, a method capable of generating
            high-quality reconstructions from a limited number of images.
            Specifically, we propose a robust initialization module that
            leverages stereo priors to aid in the recovery of camera poses
            and the reliable initialization of point clouds. Additionally, a
            diffusion-based refinement is iteratively applied to incorporate
            image diffusion priors into the Gaussian optimization process to
            preserve intricate scene details. Finally, we utilize video diffusion
            priors to further enhance the rendered images for realistic visual
            effects. Overall, our approach significantly reduces the data acquisition requirements compared to previous 3DGS methods. We
            validate the effectiveness of our framework through experiments
            on various public datasets, demonstrating its potential for high-
            quality 360-degree scene reconstruction.
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          <p><strong>Overall Framework of LM-Gaussian</strong>. . Our method takes unposed sparse images as inputs. For example, we select 8 images from the Horse Scene
          to cover a 360-degree view. Initially, we utilize a Background-Aware Depth-guided Initialization Module to generate dense point clouds and camera poses
          (see Section IV-B). These variables act as the initialization for the Gaussian kernels. Subsequently, in the Multi-modal Regularized Gaussian Reconstruction
          Module (see Section IV-C), we collectively optimize the Gaussian network through depth, normal, and virtual-view regularizations. After this stage, we train a
          Gaussian Repair model capable of enhancing Gaussian-rendered new view images. These improved images serve as guides for the training network, iteratively
          restoring Gaussian details (see Section IV-D). Finally, we employ a scene enhancement module to further enhance the rendered images for realistic visual
          effects (see Section IV-E).</p>

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    <h2 class="title">BibTeX</h2>
    <pre><code>@article{yu2024lmgaussian,
  author    = {Hanyang Yu, Xiaoxiao Long, Ping Tan},
  title     = {LM-Gaussian: Boost Sparse-view 3D Gaussian Splatting with Large Model Priors},
  journal   = {},
  year      = {2024},
}</code></pre>
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