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<h1 class="title is-1 publication-title">PreF3R: Pose-Free Feed-Forward 3D Gaussian Splatting from Variable-length Image Sequence</h1>
<div class="is-size-5 publication-authors">
<span class="author-block">
<a href="https://github.com/Svithjod">Zequn Chen</a>,</span>
<span class="author-block">
<a href="https://stephenjyang.com/">Jiezhi Yang</a>,</span>
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<a href="https://hankyang.seas.harvard.edu/">Heng Yang</a></span>
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<a href="http://sofienbouaziz.com">Sofien Bouaziz</a><sup>2</sup>,
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<a href="https://www.danbgoldman.com">Dan B Goldman</a><sup>2</sup>,
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<a href="https://homes.cs.washington.edu/~seitz/">Steven M. Seitz</a><sup>1,2</sup>,
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<h2 class="subtitle has-text-centered">
Given a sequence of unposed images of variable length, <span class="dnerf">PreF3R</span> incrementally reconstructs a set of 3D Gaussian primitives in a single feed-forward pass.
</h2>
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<h2 class="title is-3">Abstract</h2>
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<p>
We present <span class="dnerf">PreF3R</span>, Pose-Free Feed-forward 3D Reconstruction from an image sequence of variable length. Unlike previous approaches, <span class="dnerf">PreF3R</span> removes the need for camera calibration and reconstructs the 3D Gaussian field within a canonical coordinate frame directly from a sequence of unposed images, enabling efficient novel-view rendering.
</p>
<p>
We leverage DUSt3R's ability for pair-wise 3D structure reconstruction, and extend it to sequential multi-view input via a spatial memory network, eliminating the need for optimization-based global alignment. Additionally, <span class="dnerf">PreF3R</span> incorporates a dense Gaussian parameter prediction head, which enables subsequent novel-view synthesis with differentiable rasterization. This allows supervising our model with the combination of photometric loss and pointmap regression loss, enhancing both photorealism and structural accuracy.
</p>
<p>
Given a sequence of ordered images, <span class="dnerf">PreF3R</span> incrementally reconstructs the 3D Gaussian field at 20 FPS, therefore enabling real-time novel-view rendering. Empirical experiments demonstrate that <span class="dnerf">PreF3R</span> is an effective solution for the challenging task of pose-free feed-forward novel-view synthesis, while also exhibiting robust generalization to unseen scenes.
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Left: Frames rendered by <span class="dnerf">PreF3R</span>. Right: Ground-truth video.
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<!-- Method. -->
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<h2 class="title is-3">Method Overview</h2>
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<span class="dnerf">PreF3R</span>'s overall architecture. An ordered set of unposed images is fed into <span class="dnerf">PreF3R</span> sequentially. At timestamp t, the input frame is first encoded by a ViT-encoder, and then decoded into the query feature by the Target Decoder. The Target Decoder is intertwined with the Reference Decoder through cross-attention. Simultaneously, the query feature of the previous frame queries the memory bank to produce the fused feature, which the Reference Decoder decodes into the output feature. The output feature is then processed by the Gaussian Head and the Point Head to produce pixel-aligned Gaussian primitives.
The output from each frame is accumulated into global Gaussian primitives, enabling fast novel-view synthesis through rasterization.
</p>
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alt="Method overview."/>
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<h3 class="title is-4">Novel-view synthesis</h3>
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<p>
<span class="dnerf">PreF3R</span> operates at 20 FPS on a single H100 GPU, enabling real-time novel-view synthesis from numerous input images through differentiable rasterization.
</p>
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<img src="./static/images/long_seq.jpg"
class="interpolation-image"
alt="Interpolation end reference image."/>
</div>
<!--/ Re-rendering. -->
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<h3 class="title is-4">Incremental reconstruction</h3>
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<p>
<span class="dnerf">PreF3R</span> performs incremental Gaussian reconstruction in real-time. Left: in-domain scene reconstruction from ScanNet++; Right: out-of-domain scene reconstruction from Tanks and Temples.
</p>
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<img src="./static/images/gaussian.jpg"
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alt="Interpolation end reference image."/>
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<!--/ Re-rendering. -->
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<h2 class="title is-3">Related Links</h2>
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<p>
There's a lot of excellent work that was introduced around the same time as ours.
</p>
<p>
<a href="https://arxiv.org/abs/2104.09125">Progressive Encoding for Neural Optimization</a> introduces an idea similar to our windowed position encoding for coarse-to-fine optimization.
</p>
<p>
<a href="https://www.albertpumarola.com/research/D-NeRF/index.html">D-NeRF</a> and <a href="https://gvv.mpi-inf.mpg.de/projects/nonrigid_nerf/">NR-NeRF</a>
both use deformation fields to model non-rigid scenes.
</p>
<p>
Some works model videos with a NeRF by directly modulating the density, such as <a href="https://video-nerf.github.io/">Video-NeRF</a>, <a href="https://www.cs.cornell.edu/~zl548/NSFF/">NSFF</a>, and <a href="https://neural-3d-video.github.io/">DyNeRF</a>
</p>
<p>
There are probably many more by the time you are reading this. Check out <a href="https://dellaert.github.io/NeRF/">Frank Dellart's survey on recent NeRF papers</a>, and <a href="https://github.com/yenchenlin/awesome-NeRF">Yen-Chen Lin's curated list of NeRF papers</a>.
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<h2 class="title">BibTeX</h2>
<pre><code>@misc{chen2024pref3rposefreefeedforward3d,
title={PreF3R: Pose-Free Feed-Forward 3D Gaussian Splatting from Variable-length Image Sequence},
author={Zequn Chen and Jiezhi Yang and Heng Yang},
year={2024},
eprint={2411.16877},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2411.16877},
}</code></pre>
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