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<p class="caption" role="heading"><span class="caption-text">Getting Started</span></p>
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<ul>
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<li class="toctree-l1"><a class="reference internal" href="../../quickstart/installation.html">Installation</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../../quickstart/first_nerf.html">Training your first model</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../../quickstart/existing_dataset.html">Using existing data</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../../quickstart/custom_dataset.html">Using custom data</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../../quickstart/export_geometry.html">Export geometry</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../../quickstart/data_conventions.html">Data conventions</a></li>
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<p class="caption" role="heading"><span class="caption-text">Extensions</span></p>
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<ul>
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<li class="toctree-l1"><a class="reference internal" href="../../extensions/blender_addon.html">Blender VFX add-on</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../../extensions/maya_plugin.html">Autodesk Maya Plug-in</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../../extensions/unreal_engine.html">Exporting to Unreal Engine</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../../extensions/sdfstudio.html">SDFStudio</a></li>
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</ul>
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<p class="caption" role="heading"><span class="caption-text">NeRFology</span></p>
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<ul class="current">
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<li class="toctree-l1 current has-children"><a class="reference internal" href="index.html">Methods</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle child pages in navigation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="current">
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<li class="toctree-l2"><a class="reference internal" href="instant_ngp.html"> Instant-NGP</a></li>
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<li class="toctree-l2"><a class="reference internal" href="splat.html"> Splatfacto</a></li>
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<li class="toctree-l2"><a class="reference internal" href="splatw.html"> Splatfacto-W</a></li>
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<li class="toctree-l2"><a class="reference internal" href="in2n.html"> Instruct-NeRF2NeRF</a></li>
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<li class="toctree-l2"><a class="reference internal" href="igs2gs.html"> Instruct-GS2GS</a></li>
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<li class="toctree-l2"><a class="reference internal" href="signerf.html"> SIGNeRF</a></li>
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<li class="toctree-l2"><a class="reference internal" href="kplanes.html"> K-Planes</a></li>
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<li class="toctree-l2"><a class="reference internal" href="lerf.html"> LERF</a></li>
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<li class="toctree-l2"><a class="reference internal" href="livescene.html"> LiveScene</a></li>
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<li class="toctree-l2"><a class="reference internal" href="feature_splatting.html"> Feature-Splatting</a></li>
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<li class="toctree-l2"><a class="reference internal" href="mipnerf.html"> Mip-NeRF</a></li>
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<li class="toctree-l2"><a class="reference internal" href="nerf.html"> NeRF</a></li>
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<li class="toctree-l2 current current-page"><a class="current reference internal" href="#"> Nerfacto</a></li>
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<li class="toctree-l2"><a class="reference internal" href="nerfbusters.html"> Nerfbusters</a></li>
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<li class="toctree-l2"><a class="reference internal" href="nerfplayer.html"> NeRFPlayer</a></li>
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<li class="toctree-l2"><a class="reference internal" href="tetranerf.html"> Tetra-NeRF</a></li>
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<li class="toctree-l2"><a class="reference internal" href="tensorf.html"> TensoRF</a></li>
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<li class="toctree-l2"><a class="reference internal" href="generfacto.html"> Generfacto</a></li>
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<li class="toctree-l2"><a class="reference internal" href="nerf2gs2nerf.html"> NeRFtoGSandBack</a></li>
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<li class="toctree-l2"><a class="reference internal" href="opennerf.html"> OpenNeRF</a></li>
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</ul>
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<section class="tex2jax_ignore mathjax_ignore" id="nerfacto">
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<h1>Nerfacto<a class="headerlink" href="#nerfacto" title="Permalink to this heading">#</a></h1>
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<h4>Our *defacto* method.</h4>
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<section id="running-the-method">
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<h2>Running the Method<a class="headerlink" href="#running-the-method" title="Permalink to this heading">#</a></h2>
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<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>ns-train<span class="w"> </span>nerfacto<span class="w"> </span>--help
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</pre></div>
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</div>
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<p>We provide a few additional variants:</p>
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<div class="table-wrapper colwidths-auto docutils container">
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<table class="docutils align-default">
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<thead>
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<tr class="row-odd"><th class="head"><p>Method</p></th>
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<th class="head"><p>Description</p></th>
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<th class="head"><p>Memory</p></th>
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<th class="head"><p>Speed</p></th>
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</tr>
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</thead>
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<tbody>
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<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">nerfacto</span></code></p></td>
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<td><p>Default Model</p></td>
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<td><p>~6GB</p></td>
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<td><p>Fast</p></td>
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</tr>
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<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">nerfacto-big</span></code></p></td>
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<td><p>Larger higher quality</p></td>
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<td><p>~12GB</p></td>
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<td><p>Slower</p></td>
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</tr>
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<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">nerfacto-huge</span></code></p></td>
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<td><p>Even larger and higher quality</p></td>
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<td><p>~24GB</p></td>
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<td><p>Slowest</p></td>
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</tr>
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<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">depth-nerfacto</span></code></p></td>
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<td><p>Supervise on depth</p></td>
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<td><p>~6GB</p></td>
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<td><p>Fast</p></td>
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</tr>
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</tbody>
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</table>
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</div>
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</section>
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<section id="method">
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<h2>Method<a class="headerlink" href="#method" title="Permalink to this heading">#</a></h2>
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<section id="overview">
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<h3>Overview<a class="headerlink" href="#overview" title="Permalink to this heading">#</a></h3>
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<p>We created the nerfacto model to act as our default for real data captures of static scenes. The model is not existing published work, but rather a combination of many published methods that we have found work well for real data. This guide discusses the details of the model, understanding the <a class="reference internal" href="nerf.html"><span class="doc std std-doc">NeRF model</span></a> is a prerequisite.</p>
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<div class="admonition-tldr admonition">
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<p class="admonition-title">TLDR</p>
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<p>We combine the following techniques in this model:</p>
|
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<ul class="simple">
|
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<li><p>Camera pose refinement</p></li>
|
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<li><p>Per image appearance conditioning</p></li>
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<li><p>Proposal sampling</p></li>
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<li><p>Scene contraction</p></li>
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<li><p>Hash encoding</p></li>
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</ul>
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</div>
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<div class="admonition warning">
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<p class="admonition-title">Warning</p>
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<p>🏗️ This guide is under construction 🏗️</p>
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</div>
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</section>
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</section>
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<section id="pipeline">
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<h2>Pipeline<a class="headerlink" href="#pipeline" title="Permalink to this heading">#</a></h2>
|
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<img alt="../../_images/models_nerfacto_pipeline-light.png" class="only-light align-center" src="../../_images/models_nerfacto_pipeline-light.png" />
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<img alt="../../_images/models_nerfacto_pipeline-dark.png" class="only-dark align-center" src="../../_images/models_nerfacto_pipeline-dark.png" />
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<p>Here is an overview pipeline for nerfacto, we will walk through each component in this guide.</p>
|
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<section id="pose-refinement">
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<h3>Pose Refinement<a class="headerlink" href="#pose-refinement" title="Permalink to this heading">#</a></h3>
|
|
<p>It is not uncommon to have errors in the predicted camera poses. This is even more of a factor when using poses acquired from devices such as phones (ie. if you use the Record3D IOS app to capture data). Misaligned poses result in both cloudy artifacts in the scene and a reduction of sharpness and details. The NeRF framework allows us to backpropagate loss gradients to the input pose calculations. We can use this information to optimize and refine the poses.</p>
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</section>
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<section id="piecewise-sampler">
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<h3>Piecewise Sampler<a class="headerlink" href="#piecewise-sampler" title="Permalink to this heading">#</a></h3>
|
|
<p>We use a Piecewise sampler to produce the initial set of samples of the scene. This sampler allocates half of the samples uniformly up to a distance of 1 from the camera. The remaining samples are distributed such that the step size increases with each sample. The step size is chosen such that the <a class="reference internal" href="../model_components/visualize_samples.html"><span class="doc std std-doc">frustums</span></a> are scaled versions of themselves. By increasing the step sizes, we are able to sample distant objects while still having a dense set of samples for nearby objects.</p>
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</section>
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<section id="proposal-sampler">
|
|
<h3>Proposal Sampler<a class="headerlink" href="#proposal-sampler" title="Permalink to this heading">#</a></h3>
|
|
<p>The proposal sampler consolidates the sample locations to the regions of the scene that contribute most to the final render (typically the first surface intersection). This greatly improves reconstruction quality. The proposal network sampler requires a density function for the scene. The density function can be implemented in a variety of ways, we find that using a small fused-mlp with a hash encoding has sufficient accuracy and is fast. The proposal network sampler can be chained together with multiple density functions to further consolidate the sampling. We have found that two density functions are better than one. Larger than 2 leads to diminishing returns.</p>
|
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<section id="density-field">
|
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<h4>Density Field<a class="headerlink" href="#density-field" title="Permalink to this heading">#</a></h4>
|
|
<p>The density field only needs to represent a coarse density representation of the scene to guide sampling. Combining a hash encoding with a small fused MLP (from <a class="reference external" href="https://github.com/NVlabs/tiny-cuda-nn">tiny-cuda-nn</a>) provides a fast way to query the scene. We can make it more efficient by decreasing the encoding dictionary size and number of feature levels. These simplifications have little impact on the reconstruction quality because the density function does not need to learn high frequency details during the initial passes.</p>
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</section>
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</section>
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<section id="nerfacto-field">
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<h3>Nerfacto Field<a class="headerlink" href="#nerfacto-field" title="Permalink to this heading">#</a></h3>
|
|
<a class="only-light reference internal image-reference" href="../../_images/models_nerfacto_field-light.png"><img alt="../../_images/models_nerfacto_field-light.png" class="only-light align-center" src="../../_images/models_nerfacto_field-light.png" style="width: 600px;" /></a>
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<a class="only-dark reference internal image-reference" href="../../_images/models_nerfacto_field-dark.png"><img alt="../../_images/models_nerfacto_field-dark.png" class="only-dark align-center" src="../../_images/models_nerfacto_field-dark.png" style="width: 600px;" /></a>
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<p><a class="sd-sphinx-override sd-btn sd-text-wrap sd-btn-outline-primary reference external" href="https://github.com/nerfstudio-project/nerfstudio/blob/main/nerfstudio/models/nerfacto.py"><span>See the code!</span></a></p>
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Made with <a href="https://www.sphinx-doc.org/">Sphinx</a> and <a class="muted-link" href="https://pradyunsg.me">@pradyunsg</a>'s
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<a href="https://github.com/pradyunsg/furo">Furo</a>
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</div>
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<div class="right-details">
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<div class="icons">
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</footer>
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<aside class="toc-drawer">
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<span class="toc-title">
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On this page
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<div class="toc-tree-container">
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<div class="toc-tree">
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<ul>
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<li><a class="reference internal" href="#">Nerfacto</a><ul>
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<li><a class="reference internal" href="#running-the-method">Running the Method</a></li>
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<li><a class="reference internal" href="#method">Method</a><ul>
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<li><a class="reference internal" href="#overview">Overview</a></li>
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</ul>
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</li>
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<li><a class="reference internal" href="#pipeline">Pipeline</a><ul>
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<li><a class="reference internal" href="#pose-refinement">Pose Refinement</a></li>
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<li><a class="reference internal" href="#piecewise-sampler">Piecewise Sampler</a></li>
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<li><a class="reference internal" href="#proposal-sampler">Proposal Sampler</a><ul>
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<li><a class="reference internal" href="#density-field">Density Field</a></li>
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</ul>
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</li>
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<li><a class="reference internal" href="#nerfacto-field">Nerfacto Field</a></li>
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</ul>
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</li>
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</ul>
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</li>
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</ul>
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