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<section class="tex2jax_ignore mathjax_ignore" id="nerfbusters">
<h1>Nerfbusters<a class="headerlink" href="#nerfbusters" title="Permalink to this heading">#</a></h1>
<h4>Removing Ghostly Artifacts from Casually Captured NeRFs 👻 </h4>
<p><a class="sd-sphinx-override sd-btn sd-text-wrap sd-btn-outline-primary reference external" href="https://ethanweber.me/nerfbusters"><span>Paper Website</span></a></p>
<p><a class="sd-sphinx-override sd-btn sd-text-wrap sd-btn-outline-primary reference external" href="https://github.com/ethanweber/nerfbusters"><span>Code and Data</span></a></p>
<p><strong>TLDR: We present a method that uses a 3D diffusion prior to clean NeRFs and an evaluation procedure for in-the-wild NeRFs</strong></p>
<video id="teaser" muted autoplay playsinline loop controls width="100%">
<source id="mp4" src="https://ethanweber.me/nerfbusters/media/nerf-comparisons/car-sq.mp4" type="video/mp4">
</video>
<section id="installation">
<h2>Installation<a class="headerlink" href="#installation" title="Permalink to this heading">#</a></h2>
<p>First install nerfstudio dependencies. Then run:</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>pip<span class="w"> </span>install<span class="w"> </span>git+https://github.com/ethanweber/nerfbusters
nerfbusters-setup
ns-train<span class="w"> </span>nerfbusters<span class="w"> </span>--help
</pre></div>
</div>
<p>For more details, see the <a class="reference external" href="https://github.com/ethanweber/nerfbusters">installation instructions</a>.</p>
<section id="running-the-method">
<h3>Running the Method<a class="headerlink" href="#running-the-method" title="Permalink to this heading">#</a></h3>
<p>Please checkout the readme for the <a class="reference external" href="https://github.com/ethanweber/nerfbusters">Nerfbusters repository</a></p>
</section>
</section>
<section id="abstract">
<h2>Abstract<a class="headerlink" href="#abstract" title="Permalink to this heading">#</a></h2>
<p>Casually captured Neural Radiance Fields (NeRFs) suffer from artifacts such as floaters or flawed geometry when rendered outside the path of the training views. However, common practice is still to evaluate on every 8th frame, which does not measure rendering quality away from training views, hindering progress in volume rendering. We propose a new dataset and evaluation procedure, where two camera trajectories are recorded of the scene, one used for training, and the other for evaluation. We find that existing hand-crafted regularizers do not remove floaters nor improve scene geometry in this more challenging in-the-wild setting. To this end, we propose a learned, local 3D diffusion prior and a novel density score distillation sampling loss. We show that this learned prior removes floaters and improves scene geometry for casual captures.</p>
</section>
<section id="nerf-evaluation-for-casually-captured-videos">
<h2>NeRF evaluation for casually captured videos<a class="headerlink" href="#nerf-evaluation-for-casually-captured-videos" title="Permalink to this heading">#</a></h2>
<p>We train on a single camera trajectory and evaluate on a second camera trajectory. Both methods succeed on the training trajectory, but only our method succeeds on the evaluation trajectory.</p>
<video id="teaser" muted autoplay playsinline loop no-controls width="100%">
<source id="mp4" src="https://ethanweber.me/nerfbusters/media/teaser-animated.m4v" type="video/mp4">
</video>
<section id="model-overview">
<h3>Model Overview<a class="headerlink" href="#model-overview" title="Permalink to this heading">#</a></h3>
<p>We learn a local 3D prior with a diffusion model that regularizes the 3D geometry of NeRFs. We use importance sampling to query a cube with NeRF densities. We binarize these densities and perform one single denoising step using a pre-trained 3D diffusion model. With these denoised densities, we compute a density score distillation sampling (DSDS) that penalizes NeRF densities where the diffusion model predicts empty voxels and pushes the NeRF densities above the target w where the diffusion model predicts occupied voxels.</p>
<p><img alt="Overview" src="https://ethanweber.me/nerfbusters/media/method.png" /></p>
</section>
<section id="visibiltiy-loss">
<h3>Visibiltiy Loss<a class="headerlink" href="#visibiltiy-loss" title="Permalink to this heading">#</a></h3>
<p>Our visibility loss enables stepping behind or outside the training camera frustums. We accomplish this by supervising densities to be low when not seen by at least one training view. Other solutions would be to store an occupancy grid or compute ray-frustum intersection tests during rendering. Our solution is easy to implement and applicable to any NeRF.</p>
<p><img alt="Visibility" src="https://ethanweber.me/nerfbusters/media/visibility_loss.png" /></p>
</section>
<section id="results-and-dataset-preview">
<h3>Results and dataset preview<a class="headerlink" href="#results-and-dataset-preview" title="Permalink to this heading">#</a></h3>
<p>For results and a dataset preview, view the <a class="reference external" href="https://ethanweber.me/nerfbusters">project page</a>!</p>
</section>
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<ul>
<li><a class="reference internal" href="#">Nerfbusters</a><ul>
<li><a class="reference internal" href="#installation">Installation</a><ul>
<li><a class="reference internal" href="#running-the-method">Running the Method</a></li>
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<li><a class="reference internal" href="#abstract">Abstract</a></li>
<li><a class="reference internal" href="#nerf-evaluation-for-casually-captured-videos">NeRF evaluation for casually captured videos</a><ul>
<li><a class="reference internal" href="#model-overview">Model Overview</a></li>
<li><a class="reference internal" href="#visibiltiy-loss">Visibiltiy Loss</a></li>
<li><a class="reference internal" href="#results-and-dataset-preview">Results and dataset preview</a></li>
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