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GaussianSR
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<!-- paper title -->
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<font>GaussianSR</font>: 3D Gaussian Super-Resolution with 2D Diffusion Priors
</p>
<!-- publication -->
<!-- <p class="subtitle is-4"> Arxiv 2023 </p> -->
<!-- authors -->
<p class="title is-4 publication-authors">
<span class="author-block">Xiqian Yu*</a><sup>1</sup>,</span>
<span class="author-block">Hanxin Zhu*</a><sup>1</sup>,</span>
<span class="author-block">Tianyu He</a><sup>2</sup>,</span>
<span class="author-block">Zhibo Chen</a><sup>1</sup></span>
</p>
<!-- affiliations -->
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<sup>*</sup> Denotes equal contribution </br>
</p>
<p class="subtitle is-5 publication-authors">
<span class="author-block"><sup>1</sup> University of Science and Technology of China</span>,
<span class="author-block"><sup>2</sup> Microsoft Research Asia</span>
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<!-- <section class="hero is-light is-small">
<div class="hero-body"> -->
<!-- <iframe id="demo-animelake" src='https://my.spline.design/untitled-c450ec593848b5ebf5731ad59fc9636d/' frameborder='0' width="49.5%" height="576"></iframe> <iframe id="demo-animelake" src='https://my.spline.design/untitled-79a5f969bd57796761dd84fe987d99ef/' frameborder='0' width="49.5%" height="576"></iframe> -->
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<p class="content has-text-centered is-size-5">
Our method can generate navigatable 3D scenes out of a single text prompt of a single image.</br>
<b>Click and drag</b> (navigate) / <b>shift and scroll</b> (zoom) <b>to feel the 3D.</b>
</p>
</div>
</section> -->
<!-- -->
<!-- -->
<!-- [> main container <] -->
<!-- <section class="section"> -->
<div class="container is-max-desktop has-text-centered">
<!-- abstract -->
<p class="title is-3 mt-5 has-text-centered"> Abstract </p>
<p class="content is-size-6 has-text-justified">
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.
</p>
<!-- main figure -->
<p class="title is-3 mt-5 has-text-centered"> Method </p>
<img src="images/framework.png", alt="Algorithm description of GaussainSR", width="100%", height="100%"> </img>
<p class="content has-text-centered is-size-5">
GaussianSR 1) synthesizes high-resolution novel views by <b>distilling
2D diffusion priors</b> into 3D representation with SDS
to alleviate the lack of high-resolution data; 2) proposes <b>Gaussian Dropout and Diffusion
Timestep Annealing</b> to reduce stochastic disturbance introduced by SDS.
</p>
<p class="title is-3 mt-5 has-text-centered"> Qualitative Comparison </p>
<img src="images/main_result_mip360.png", alt="Perceptual quality comparison", width="100%", height="100%"> </img>
<p class="content has-text-centered is-size-5">
GaussainSR can produce superior visual results characterized by clearer edges and sharper details
compared to other SOTA methods.
</p>
<p class="title is-3 mt-5 has-text-centered"> Quantitative Comparison </p>
<p class="content is-size-6 has-text-justified">
<!-- The quality of view synthesis is assessed relative to the ground truth from the same pose, employing four metrics:
Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM), LPIPS (VGG) and Frames Per Second (FPS). -->
We compare our method against several prior approaches on both synthetic and real-world datasets.
GaussainSR outperforms previous state-of-the-art methods significantly in terms of PSNR,
SSIM, and LPIPS metrics, while also requiring less rendering time.
</p>
<table>
<thead>
<tr>
<!-- <th rowspan="2">Method</th>
<th colspan="4">Blender Dataset</th>
<th colspan="4">Mip-NeRF 360 Dataset</th>
<th colspan="4">Deep Blending Dataset</th> -->
<!-- <th rowspan="2">Method</th> -->
<!-- <td rowspan="2"><strong>Method</strong></td> -->
<td colspan="1"><strong></strong></td>
<td colspan="4"><strong>Blender Dataset</strong></td>
<td colspan="4"><strong>Mip-NeRF 360 Dataset</strong></td>
<td colspan="4"><strong>Deep Blending Dataset</strong></td>
</tr>
<tr>
<th>Method</th>
<th>PSNR ↑</th>
<th>SSIM ↑</th>
<th>LPIPS ↓</th>
<th>FPS ↑</th>
<th>PSNR ↑</th>
<th>SSIM ↑</th>
<th>LPIPS ↓</th>
<th>FPS ↑</th>
<th>PSNR ↑</th>
<th>SSIM ↑</th>
<th>LPIPS ↓</th>
<th>FPS ↑</th>
</tr>
</thead>
<tbody>
<tr>
<td>3DGS</td>
<td>21.78</td>
<td>0.868</td>
<td>0.104</td>
<td><strong>192</strong></td>
<td>20.28</td>
<td>0.581</td>
<td>0.420</td>
<td><strong>33</strong></td>
<td>26.64</td>
<td>0.854</td>
<td>0.312</td>
<td><strong>60</strong></td>
</tr>
<tr>
<td>StableSR</td>
<td>23.57</td>
<td>0.854</td>
<td>0.207</td>
<td><1</td>
<td>21.83</td>
<td>0.467</td>
<td>0.383</td>
<td><1</td>
<td>23.93</td>
<td>0.708</td>
<td>0.325</td>
<td><1</td>
</tr>
<tr>
<td>Bicubic</td>
<td>27.23</td>
<td>0.911</td>
<td>0.115</td>
<td>93</td>
<td>25.14</td>
<td>0.618</td>
<td>0.406</td>
<td>27</td>
<td>28.01</td>
<td>0.864</td>
<td>0.330</td>
<td>40</td>
</tr>
<tr>
<td>NeRF-SR</td>
<td>27.81</td>
<td>0.920</td>
<td>0.097</td>
<td><1</td>
<td>--</td>
<td>--</td>
<td>--</td>
<td>--</td>
<td>--</td>
<td>--</td>
<td>--</td>
<td>--</td>
</tr>
<tr>
<td><strong>GaussianSR(Ours)</strong></td>
<td><strong>28.37</strong></td>
<td><strong>0.924</strong></td>
<td><strong>0.087</strong></td>
<td><strong>192</strong></td>
<td><strong>25.60</strong></td>
<td><strong>0.663</strong></td>
<td><strong>0.368</strong></td>
<td><strong>33</strong></td>
<td><strong>28.28</strong></td>
<td><strong>0.873</strong></td>
<td><strong>0.307</strong></td>
<td><strong>60</strong></td>
</tr>
</tbody>
</table>
</div>
</section>
<section class="section">
<pre><code>
@article{yu2024gaussiansr,
title={GaussianSR: 3D Gaussian Super-Resolution with 2D Diffusion Priors},
author={Yu, Xiqian and Zhu, Hanxin and He, Tianyu and Chen, Zhibo},
journal={arXiv preprint arXiv:2406.10111},
year={2024}
}
</code></pre>
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