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<h1 class="title is-1 publication-title">Data Augmentation for NeRFs in the Low Data Limit</h1>
<div class="is-size-5 publication-authors">
<span class="author-block">
<a href="https://agaggar.github.io">Ayush Gaggar</a>,</span>
<span class="author-block">
<a href="https://murpheylab.github.io/people/toddmurphey">Todd Murphey</a></span>
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<span class="author-block">Northwestern University, Center for Robotics & Biosystems</span>
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<p>
We propose a method for augmenting training data for NeRFs trained with sparse images. We use rejection sampling to select N additional views from a distribution that captures both model-based, in-distribution and scene-based out-of-distribution uncertainty. Our method is easy to implement and can be incorporated with the Nerfstudio python package with minimal overhead.
</p>
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</div> -->
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<h2 class="subtitle has-text-centered">
<!-- <h2 class="title is-3"></h2> -->
Only our data augmentation method generates reasonable scene reconstructions without visual artifacts.
Hallucinations, occlusions, and other visual artifacts are common when training with sparse data.
a) shows ground truth, b) shows our method, c) shows hallucinations (data augmented by <a href="https://arxiv.org/abs/2311.17874">FisherRF</a> method),
and d) shows white spots (data collected by <a href="https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9913658">Entropy</a> method).
</h2>
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<h2 class="title is-3">Abstract</h2>
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<p>
Current methods based on Neural Radiance Fields fail in the low data limit, particularly when training on incomplete scene data.
</p>
<p>
Prior works augment training data only in next-best-view applications, which lead to hallucinations and model collapse with sparse data.
In contrast, we propose adding a set of views during training by rejection sampling from a posterior uncertainty distribution, generated by combining a volumetric uncertainty estimator with spatial coverage.
We validate our results on partially observed scenes; on average, our method performs 39.9% better with 87.5% less variability across established scene reconstruction benchmarks, as compared to state of the art baselines.
We further demonstrate that augmenting the training set by sampling from any distribution leads to better, more consistent scene reconstruction in sparse environments.
</p>
<p>
This work is foundational for robotic tasks where augmenting a dataset with informative data is critical in resource-constrained, a priori unknown environments.
</p>
</div>
</div>
</div>
<!-- / Abstract. -->
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<h2 class="title is-3">See how our augmentation compares with others!</h2>
<h2 class="subtitle has-text-centered">Ours vs. FisherRF</h2>
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<h2 class="title is-3">Method Comparisons</h2>
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<p><strong>Our method achieves the best median performance and lowest interquartile range compared to any other method across any other scene</strong>, except for <em>material SSIM vs. Entropy</em>, which has a lower interquartile range.
Evaluation results of standard image quality metrics across our method and three other SOTA baselines. Each metric score was evaluated across the 200 images in the evaluation dataset for each of the three scenes. A higher score is better for PSNR and SSIM, and a lower score is better for LPIPS. We achieve the best median performance and the lowest interquartile range compared to any method across each scene, except for material SSIM vs. Entropy. Our method performs better with a statistical significance of p <0.05 and a Bonferroni correction of 3, except for <em>lego LPIPS vs. Uniform</em>, <em>chair LPIPS vs FisherRF</em>, and <em>SSIM vs Uniform and FisherRF</em>.
</p>
<embed src="./static/images/metrics_comparison.pdf#view=fitbh&toolbar=0" type="application/pdf" style="width:100%; margin:0 auto; height: 480px;">
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</section>
<!-- Concurrent Work. -->
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<h2 class="title is-3">Related Links</h2>
<div class="content has-text-justified">
<p>
This work builds off a lot of excellent work.
</p>
<p>
Our architecture uses <a href="https://docs.nerf.studio/#">Nerfstudio</a>, a great Python package for end-to-end NeRF training.
</p>
<p>
<a href="https://arxiv.org/abs/2311.17874">FisherRF</a> introduces an idea similar to ours, but fails in the low data limit with partially observed scenes.
</p>
<p>
<a href="https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9913658">Lee et al.</a> initially introduce the idea of calculating Entropy using NeRF architectures, but is limited to modeling only in-distribution uncertainty.
</p>
<p>
There are probably many more by the time you are reading this. Check out <a href="https://dellaert.github.io/NeRF/">Dr. Irshad's amazing list of Robotic NeRF papers</a>, and <a href="https://arxiv.org/abs/2405.01333">this 2024 survey on NeRF papers in Robotics</a>.
</p>
</div>
</div>
</div>
<!-- / Concurrent Work. -->
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Abstract.
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<div class="column is-four-fifths">
<h2 class="title is-3">Abstract</h2>
<div class="content has-text-justified">
<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
(NeRF) by optimizing an
additional continuous volumetric deformation field that warps each observed point into a
canonical 5D NeRF.
We observe that these NeRF-like deformation fields are prone to local minima, and
propose a coarse-to-fine optimization method for coordinate-based models that allows for
more robust optimization.
By adapting principles from geometry processing and physical simulation to NeRF-like
models, we propose an elastic regularization of the deformation field that further
improves robustness.
</p>
<p>
We show that <span class="dnerf">Nerfies</span> can turn casually captured selfie
photos/videos into deformable NeRF
models that allow for photorealistic renderings of the subject from arbitrary
viewpoints, which we dub <i>"nerfies"</i>. We evaluate our method by collecting data
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>
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</div>
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/ Abstract.
Paper video.
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Visual Effects.
<div class="column">
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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%">
<source src="./static/videos/dollyzoom-stacked.mp4"
type="video/mp4">
</video>
</div>
</div>
/ Visual Effects.
Matting.
<div class="column">
<h2 class="title is-3">Matting</h2>
<div class="columns is-centered">
<div class="column content">
<p>
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>
<video id="matting-video" controls playsinline height="100%">
<source src="./static/videos/matting.mp4"
type="video/mp4">
</video>
</div>
</div>
</div>
</div>
/ Matting.
Animation.
<div class="columns is-centered">
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<h2 class="title is-3">Animation</h2>
Interpolating.
<h3 class="title is-4">Interpolating states</h3>
<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.
</p>
</div>
<div class="columns is-vcentered interpolation-panel">
<div class="column is-3 has-text-centered">
<img src="./static/images/interpolate_start.jpg"
class="interpolation-image"
alt="Interpolate start reference image."/>
<p>Start Frame</p>
</div>
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<div id="interpolation-image-wrapper">
Loading...
</div>
<input class="slider is-fullwidth is-large is-info"
id="interpolation-slider"
step="1" min="0" max="100" value="0" type="range">
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<img src="./static/images/interpolate_end.jpg"
class="interpolation-image"
alt="Interpolation end reference image."/>
<p class="is-bold">End Frame</p>
</div>
</div>
<br/>
/ Interpolating.
Re-rendering.
<h3 class="title is-4">Re-rendering the input video</h3>
<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.
</p>
</div>
<div class="content has-text-centered">
<video id="replay-video"
controls
muted
preload
playsinline
width="75%">
<source src="./static/videos/replay.mp4"
type="video/mp4">
</video>
</div>
/ Re-rendering.
</div>
</div>
/ Animation.
Concurrent Work.
<div class="columns is-centered">
<div class="column is-full-width">
<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>.
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/ Concurrent Work.
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<h2 class="title">BibTeX</h2>
<pre><code>@article{park2021nerfies,
author = {Park, Keunhong and Sinha, Utkarsh and Barron, Jonathan T. and Bouaziz, Sofien and Goldman, Dan B and Seitz, Steven M. and Martin-Brualla, Ricardo},
title = {Nerfies: Deformable Neural Radiance Fields},
journal = {ICCV},
year = {2021},
}</code></pre>
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