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<h1 class="title is-2 publication-title">CoR-GS: Sparse-View 3D Gaussian Splatting via Co-Regularization</h1>
<div class="is-size-5 publication-authors">
<span class="author-block">
<a href="https://scholar.google.com/citations?user=WeRMtW4AAAAJ&hl=en" target="_blank">
Jiawei Zhang</a><sup>1</sup>,
</span>
<span class="author-block">
<a href="https://scholar.google.com/citations?hl=en&user=XG-o7LUAAAAJ" target="_blank">
Jiahe Li</a><sup>1</sup>,
</span>
<span class="author-block">
<a href="https://scholar.google.com/citations?user=3PURN9QAAAAJ&hl=zh-CN&authuser=1&oi=ao" target="_blank">
Xiaohan Yu</a><sup>3</sup>,
</span>
<span class="author-block">
<a href="https://scholar.google.com/citations?user=yTshbKkAAAAJ&hl=zh-CN&authuser=1" target="_blank">
Huang Lei</a><sup>2</sup>,
</span>
<span class="author-block">
<a href="https://scholar.google.com/citations?user=gIEZe5IAAAAJ&hl=en" target="_blank">
Lin Gu</a><sup>4,5</sup>,
</span>
<span class="author-block">
<a>
Jin Zheng</a><sup>1</sup>,
</span>
<span class="author-block">
<a href="https://scholar.google.com/citations?user=k6l1vZIAAAAJ&hl=en" target="_blank">
Xiao Bai</a><sup>1,*</sup>,
</span>
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<div class="is-size-5 publication-authors">
<span class="author-block"><sup>1</sup>Beihang University,</span>
<span class="author-block"><sup>2</sup>SKLCCSE, Institute of Artificial Intelligence, Beihang University,</span>
<span class="author-block"><sup>3</sup>School of Computing, Macquarie University,</span>
</div>
<div class="is-size-5 publication-authors">
<span class="author-block"><sup>4</sup>RIKEN AIP,</span>
<span class="author-block"><sup>5</sup>The University of Tokyo</span>
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<h2 class="subtitle has-text-centered">
Illustration of how the different behaviors between two 3D Gaussian radiance fields correlated to construction quality. Gaussians with different behaviors tend to not fit the ground-truth shape well. Therefore, inaccurate reconstructions can be identified by measuring the differences without accessing ground-truth information.
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<h2 class="title is-3">Abstract</h2>
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<p>
3D Gaussian Splatting (3DGS) creates a radiance field consisting of 3D Gaussians to represent a scene. With sparse training views, 3DGS easily suffers from overfitting, negatively impacting the reconstruction quality.</p>
<p>
This paper introduces a new co-regularization perspective for improving sparse-view 3DGS. When training two 3D Gaussian radiance fields with the same sparse views of a scene, we observe that the two radiance fields exhibit \textit{point disagreement} and \textit{rendering disagreement} that can unsupervisedly predict reconstruction quality, stemming from the sampling implementation in densification. We further quantify the point disagreement and rendering disagreement by evaluating the registration between Gaussians' point representations and calculating differences in their rendered pixels. The empirical study demonstrates the negative correlation between the two disagreements and accurate reconstruction, which allows us to identify inaccurate reconstruction without accessing ground-truth information.
</p>
<p>
Based on the study, we propose CoR-GS, which identifies and suppresses inaccurate reconstruction based on the two disagreements: (\romannumeral1) Co-pruning considers Gaussians that exhibit high point disagreement in inaccurate positions and prunes them. (\romannumeral2) Pseudo-view co-regularization considers pixels that exhibit high rendering disagreement are inaccurately rendered and suppress the disagreement.
</p>
Results on LLFF, Mip-NeRF360, DTU, and Blender demonstrate that CoR-GS effectively regularizes the scene geometry, reconstructs the compact representations, and achieves state-of-the-art novel view synthesis quality under sparse training views.
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<h2 class="title is-3">Method</h2>
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<p>
CoR-GS trains two 3D Gaussian radiance fields with the same views and conducts co-regularization during training. It improves sparse-view 3DGS by identifying and suppressing inaccurate reconstruction based on the point disagreement and rendering disagreement.
</p>
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<h3 class="title is-3">Comparison</h3>
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Comarison with current SOTA baselines. Zoom in for better visualization.
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<h5 class="title is-5">LLFF</h5>
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<pre><code>@article{li2024dngaussian,
title={DNGaussian: Optimizing Sparse-View 3D Gaussian Radiance Fields with Global-Local Depth Normalization},
author={Jiahe Li and Jiawei Zhang and Xiao Bai and Jin Zheng and Xin Ning and Jun Zhou and Lin Gu},
journal={arXiv preprint arXiv:2403.06912},
year={2024}
}</code></pre>
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