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<h1 class="title is-1 publication-title">EndoGaussian: Gaussian Splatting for Deformable Surgical Scene Reconstruction</h1>
<div class="is-size-5 publication-authors">
<span class="author-block">
<a href="https://yifliu3.github.io/">Yifan Liu*</a>,</span>
<span class="author-block">
<a href="https://xggnet.github.io/">Chenxin Li*</a>,</span>
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<a href="https://scholar.google.com.hk/citations?user=C6fAQeIAAAAJ&hl=en">Chen Yang</a>,</span>
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<a href="https://www.ee.cuhk.edu.hk/~yxyuan/people/people.htm">Yixuan Yuan</a>
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<h2 class="subtitle has-text-centered">
<p>
<span class="dnerf">EndoGaussian</span>
reconstructs surgical scene under real-time rendering efficacy (195 FPS real-time, 100x gain), better rendering quality
(35+ PSNR), and less training overhead (within 2 min/scene).
</p>
<!-- <p>
Below is the real video and our reconstruction of the "pull" and "cut" motions.
</p> -->
<!-- , paving the way for real-time intraoperative application. -->
<!-- turns selfie videos from your phone into free-viewpoint portraits. -->
</h2>
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<p>
The ground truth and the corresponding reconstruction by our method for the "pulling" and "cutting" clips.
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<h2 class="title is-3">Abstract</h2>
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<!-- <p>
We present the first method capable of photorealistically reconstructing a non-rigidly
deforming scene using photos/videos captured casually from mobile phones.
</p>
<p>
Our approach augments neural radiance fields
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additional continuous volumetric deformation field that warps each observed point into a
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models that allow for photorealistic renderings of the subject from arbitrary
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using a
rig with two mobile phones that take time-synchronized photos, yielding train/validation
images of the same pose at different viewpoints. We show that our method faithfully
reconstructs non-rigidly deforming scenes and reproduces unseen views with high
fidelity.
</p> -->
<p>
Reconstructing deformable tissues from endoscopic stereo videos is essential in many downstream surgical applications. However, existing methods suffer from slow inference speed, which greatly limits their practical use.
</p>
<p>
In this paper, we introduce EndoGaussian, a real-time surgical scene reconstruction framework that builds on 3D Gaussian Splatting. Our framework represents dynamic surgical scenes as canonical Gaussians and a time-dependent deformation field, which predicts Gaussian deformations at novel timestamps. Due to the efficient Gaussian representation and parallel rendering pipeline, our framework significantly accelerates the rendering speed compared to previous methods. In addition, we design the deformation field as the combination of a lightweight encoding voxel and an extremely tiny MLP, allowing for efficient Gaussian tracking with a minor rendering burden. Furthermore, we design a holistic Gaussian initialization method to fully leverage the surface distribution prior, achieved by searching informative points from across the input image sequence.
</p>
<p>
Experiments on public endoscope datasets demonstrate that our method can achieve real-time rendering speed (195 FPS real-time, 100x gain) while maintaining the state-of-the-art reconstruction quality (35.925 PSNR) and the fastest training speed (within 2 min/scene), showing significant promise for intraoperative surgery applications.
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<!--/ Abstract. -->
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<!-- Visual Effects. -->
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<h2 class="title is-3">Visual Effects</h2>
<p>
Using <i>nerfies</i> you can create fun visual effects. This Dolly zoom effect
would be impossible without nerfies since it would require going through a wall.
</p>
<video id="dollyzoom" autoplay controls muted loop playsinline height="100%">
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As a byproduct of our method, we can also solve the matting problem by ignoring
samples that fall outside of a bounding box during rendering.
</p>
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<source src="./static/videos/matting.mp4"
type="video/mp4">
</video>
</div>
</div>
</div>
</div> -->
<!--/ Matting. -->
<!-- Re-rendering. -->
<h3 class="title is-3">How EndoGaussian facilitate real-time intraoperative application?</h3>
<img src="introduction_new.png" alt="xxxx">
<div class="content has-text-justified">
<p>
Using <span class="dnerf">EndoGaussian</span>, you can perform deformable surgical scene reconstruction with nearly 200 FPS, 35+ PSNR.
</p>
</div>
<!-- Animation. -->
<div class="columns is-centered">
<div class="column is-full-width">
<h3 class="title is-3">Method Overview</h3>
<!-- Interpolating. -->
<!-- <h3 class="title is-4">Interpolating states</h3> -->
<img src="framework_new.png" alt="xxxx">
<div class="content has-text-justified">
<p>
<!-- We can also animate the scene by interpolating the deformation latent codes of two input
frames. Use the slider here to linearly interpolate between the left frame and the right
frame. -->
llustration of the proposed EndoGaussian framework, which consists of a) Holistic Gaussian Initialization, b) Voxel-based Gaussian Tracking, and c) Optimization.
</p>
</div>
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<img src="./static/images/interpolate_start.jpg"
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alt="Interpolate start reference image."/>
<p>Start Frame</p>
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Loading...
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<br/> -->
<!--/ Interpolating. -->
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<!--/ Re-rendering. -->
</div>
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<!--/ Animation. -->
<!-- Concurrent Work. -->
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<h2 class="title is-3">Related Links</h2>
<div class="content has-text-justified">
<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>.
</p>
</div>
</div>
</div> -->
<!--/ Concurrent Work. -->
<!-- </div> -->
<h3 class="title is-3">Visual Results</h3>
<img src="comparison_new.png" alt="xxxx">
<div class="content has-text-justified">
<p>
<!-- Using <span class="dnerf">Nerfies</span>, you can re-render a video from a novel
viewpoint such as a stabilized camera by playing back the training deformations. -->
Display of rendered results of our EndoGaussian against prior SOTA methods on surgical scene reconstruction. The rendering FPS, training time cost and image quality in PSNR is provided.
</p>
</div>
<!-- <div class="content has-text-centered">
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controls
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playsinline
width="75%">
<source src="./static/videos/replay.mp4"
type="video/mp4">
</video>
</div> -->
<!--/ Re-rendering. -->
</div>
</div>
</section>
<section class="section" id="BibTeX">
<div class="container is-max-desktop content">
<h2 class="title">BibTeX</h2>
<pre><code>@article{liu2024endogaussian,
title={EndoGaussian: Gaussian Splatting for Deformable Surgical Scene Reconstruction},
author={Liu, Yifan and Li, Chenxin and Yang, Chen and Yuan, Yixuan},
journal={arXiv preprint arXiv:2401.12561},
year={2024}
}
</code></pre>
</div>
</section>
<section class="section" id="Relevant Works">
<div class="container is-max-desktop content">
<h2 class="title">Relevant Works</h2>
<hr style="margin-top:0px">
<div class="paper">
<h2><a>Endora: Video Generation Models as Endoscopy Simulators</a></h2>
<p>A pioneering exploration into high-fidelity medical video generation on endoscopy scenes.</p>
<p>
<a href="https://endora-medvidgen.github.io/">[Page]</a> | <a href="https://arxiv.org/abs/2403.11050">[Paper]</a> | <a href="https://github.com/XGGNet/Endora">[Code]</a>
</p>
</div>
<hr style="margin-top:0px">
<div class="paper">
<h2><a>U-KAN Makes Strong Backbone for Medical Image Segmentation and Generation</a></h2>
<p>An innovative enhancement of U-Net for medical image tasks using Kolmogorov-Arnold Network (KAN).</p>
<p>
<a href="https://yes-ukan.github.io/">[Page]</a> | <a href="https://arxiv.org/pdf/2406.02918">[Paper]</a> | <a href="https://github.com/CUHK-AIM-Group/U-KAN">[Code]</a>
</p>
</div>
<hr>
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</section>
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