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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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<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-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"><a class="reference internal" href="nerfacto.html"> 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="pynerf.html"> PyNeRF</a></li>
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<li class="toctree-l2"><a class="reference internal" href="seathru_nerf.html"> SeaThru-NeRF</a></li>
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<li class="toctree-l2"><a class="reference internal" href="zipnerf.html"> Zip-NeRF</a></li>
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<li class="toctree-l2"><a class="reference internal" href="bionerf.html"> BioNeRF</a></li>
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<li class="toctree-l2 current current-page"><a class="current reference internal" href="#"> 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="from-nerfs-to-gaussian-splats-and-back">
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<h1>From NeRFs to Gaussian Splats, and Back<a class="headerlink" href="#from-nerfs-to-gaussian-splats-and-back" title="Permalink to this heading">#</a></h1>
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<p>This is the implementation of <a class="reference external" href="https://arxiv.org/abs/2405.09717">From NeRFs to Gaussian Splats, and Back</a>; An efficient procedure to convert back and forth between NeRF and GS, and thereby get the best of both approaches. New dataset in the paper can be downloaded from <a class="reference external" href="https://drive.google.com/drive/folders/1xvbONL4EVgHxaHMsV101455l_jNgyaUM?usp=sharing">this google drive link</a>. The official code can be found <a class="reference external" href="https://github.com/grasp-lyrl/NeRFtoGSandBack">here</a>.</p>
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<video id="teaser" muted autoplay playsinline loop controls width="100%">
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<source id="mp4" src="https://github.com/grasp-lyrl/NeRFtoGSandBack/blob/main/assets/Giannini_demo_compressed.mp4" type="video/mp4">
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</video>
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<section id="installation">
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<h2>Installation<a class="headerlink" href="#installation" title="Permalink to this heading">#</a></h2>
|
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<p>This repository follows the nerfstudio method <a class="reference external" href="https://github.com/nerfstudio-project/nerfstudio-method-template/tree/main">template</a></p>
|
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<section id="install-nerfstudio-dependencies">
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<h3>0. Install Nerfstudio dependencies<a class="headerlink" href="#install-nerfstudio-dependencies" title="Permalink to this heading">#</a></h3>
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<p>Please follow the Nerfstudio <a class="reference external" href="https://docs.nerf.studio/quickstart/installation.html">installation guide</a> to create an environment and install dependencies.</p>
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</section>
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<section id="install-the-repository">
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<h3>1. Install the repository<a class="headerlink" href="#install-the-repository" title="Permalink to this heading">#</a></h3>
|
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<p>Clone and navigate into this repository. Run the following commands:</p>
|
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<p><code class="docutils literal notranslate"><span class="pre">pip</span> <span class="pre">install</span> <span class="pre">-e</span> <span class="pre">nerfsh</span></code></p>
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<p>and</p>
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<p><code class="docutils literal notranslate"><span class="pre">pip</span> <span class="pre">install</span> <span class="pre">-e</span> <span class="pre">nerfgs</span></code>.</p>
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<p>Finally, run <code class="docutils literal notranslate"><span class="pre">ns-install-cli</span></code>.</p>
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</section>
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<section id="check-installation">
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<h3>2. Check installation<a class="headerlink" href="#check-installation" title="Permalink to this heading">#</a></h3>
|
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<p>Run <code class="docutils literal notranslate"><span class="pre">ns-train</span> <span class="pre">--help</span></code>. You should be able to find two methods, <code class="docutils literal notranslate"><span class="pre">nerfsh</span></code> and <code class="docutils literal notranslate"><span class="pre">nerfgs</span></code>, in the list of methods.</p>
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</section>
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</section>
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<section id="downloading-data">
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<h2>Downloading data<a class="headerlink" href="#downloading-data" title="Permalink to this heading">#</a></h2>
|
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<p>You could download the Giannini-Hall and aspen datasets from <a class="reference external" href="https://drive.google.com/drive/folders/19TV6kdVGcmg3cGZ1bNIUnBBMD-iQjRbG">this google drive link</a>. Our new dataset (Wissahickon and Locust-Walk) can be downloaded from <a class="reference external" href="https://drive.google.com/drive/folders/1xvbONL4EVgHxaHMsV101455l_jNgyaUM?usp=sharing">this google drive link</a>.</p>
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</section>
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<section id="nerfgs-nerfs-to-gaussian-splats">
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<h2>NeRFGS: NeRFs to Gaussian Splats<a class="headerlink" href="#nerfgs-nerfs-to-gaussian-splats" title="Permalink to this heading">#</a></h2>
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<section id="training-nerf-sh">
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<h3>Training NeRF-SH<a class="headerlink" href="#training-nerf-sh" title="Permalink to this heading">#</a></h3>
|
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<p>Run the following command for training. Replace <code class="docutils literal notranslate"><span class="pre">DATA_PATH</span></code> with the data directory location.</p>
|
|
<p><code class="docutils literal notranslate"><span class="pre">ns-train</span> <span class="pre">nerfsh</span> <span class="pre">--data</span> <span class="pre">DATA_PATH</span> <span class="pre">--pipeline.model.camera-optimizer.mode</span> <span class="pre">off</span> </code></p>
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<p>To train on Wissahickon or Locust-Walk dataset, you need to add <code class="docutils literal notranslate"><span class="pre">nerfstudio-data</span> <span class="pre">--eval-mode</span> <span class="pre">filename</span></code> to properly split training and validation data, i.e.,</p>
|
|
<p><code class="docutils literal notranslate"><span class="pre">ns-train</span> <span class="pre">nerfsh</span> <span class="pre">--data</span> <span class="pre">DATA_PATH</span> <span class="pre">--pipeline.model.camera-optimizer.mode</span> <span class="pre">off</span> <span class="pre">nerfstudio-data</span> <span class="pre">--eval-mode</span> <span class="pre">filename</span></code></p>
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</section>
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<section id="nerfgs-converting-nerf-sh-to-guassian-splats">
|
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<h3>NeRFGS: Converting NeRF-SH to Guassian splats<a class="headerlink" href="#nerfgs-converting-nerf-sh-to-guassian-splats" title="Permalink to this heading">#</a></h3>
|
|
<p>Replace <code class="docutils literal notranslate"><span class="pre">CONFIG_LOCATION</span></code> with the location of config file saved after training.</p>
|
|
<p><code class="docutils literal notranslate"><span class="pre">ns-export-nerfsh</span> <span class="pre">--load-config</span> <span class="pre">CONFIG_LOCATION</span> <span class="pre">--output-dir</span> <span class="pre">exports/nerfgs/</span> <span class="pre">--num-points</span> <span class="pre">2000000</span> <span class="pre">--remove-outliers</span> <span class="pre">True</span> <span class="pre">--normal-method</span> <span class="pre">open3d</span> <span class="pre">--use_bounding_box</span> <span class="pre">False</span></code></p>
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</section>
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<section id="visualize-converted-gaussian-splats">
|
|
<h3>Visualize converted Gaussian splats<a class="headerlink" href="#visualize-converted-gaussian-splats" title="Permalink to this heading">#</a></h3>
|
|
<p>Replace <code class="docutils literal notranslate"><span class="pre">DATA_PATH</span></code> with the data directory location. You also need to add <code class="docutils literal notranslate"><span class="pre">nerfstudio-data</span> <span class="pre">--eval-mode</span> <span class="pre">filename</span></code> if train on Wissahickon or Locust-Walk.</p>
|
|
<p><code class="docutils literal notranslate"><span class="pre">ns-train</span> <span class="pre">nerfgs</span> <span class="pre">--data</span> <span class="pre">DATA_PATH</span> <span class="pre">--max-num-iterations</span> <span class="pre">1</span> <span class="pre">--pipeline.model.ply-file-path</span> <span class="pre">exports/nerfgs/nerfgs.ply</span></code></p>
|
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</section>
|
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<section id="fintuned-nerfgs">
|
|
<h3>Fintuned NeRFGS<a class="headerlink" href="#fintuned-nerfgs" title="Permalink to this heading">#</a></h3>
|
|
<p>We reduces the learning rate for finetuning. You also need to add <code class="docutils literal notranslate"><span class="pre">nerfstudio-data</span> <span class="pre">--eval-mode</span> <span class="pre">filename</span></code> if train on Wissahickon or Locust-Walk.</p>
|
|
<p><code class="docutils literal notranslate"><span class="pre">ns-train</span> <span class="pre">nerfgs</span> <span class="pre">--data</span> <span class="pre">DATA_PATH</span> <span class="pre">--pipeline.model.ply-file-path</span> <span class="pre">exports/nerfgs/nerfgs.ply</span> <span class="pre">--max-num-iterations</span> <span class="pre">5000</span> <span class="pre">--pipeline.model.sh-degree-interval</span> <span class="pre">0</span> <span class="pre">--pipeline.model.warmup-length</span> <span class="pre">100</span> <span class="pre">--optimizers.xyz.optimizer.lr</span> <span class="pre">0.00001</span> <span class="pre">--optimizers.xyz.scheduler.lr-pre-warmup</span> <span class="pre">0.0000001</span> <span class="pre">--optimizers.xyz.scheduler.lr-final</span> <span class="pre">0.0000001</span> <span class="pre">--optimizers.features-dc.optimizer.lr</span> <span class="pre">0.01</span> <span class="pre">--optimizers.features-rest.optimizer.lr</span> <span class="pre">0.001</span> <span class="pre">--optimizers.opacity.optimizer.lr</span> <span class="pre">0.05</span> <span class="pre">--optimizers.scaling.optimizer.lr</span> <span class="pre">0.01</span> <span class="pre">--optimizers.rotation.optimizer.lr</span> <span class="pre">0.0000000001</span> <span class="pre">--optimizers.camera-opt.optimizer.lr</span> <span class="pre">0.0000000001</span> <span class="pre">--optimizers.camera-opt.scheduler.lr-pre-warmup</span> <span class="pre">0.0000000001</span> <span class="pre">--optimizers.camera-opt.scheduler.lr-final</span> <span class="pre">0.0000000001</span></code></p>
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</section>
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</section>
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<section id="gsnerf-gaussian-splats-to-nerfs">
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<h2>GSNeRF: Gaussian Splats to NeRFs<a class="headerlink" href="#gsnerf-gaussian-splats-to-nerfs" title="Permalink to this heading">#</a></h2>
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<section id="scene-modification">
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<h3>Scene modification<a class="headerlink" href="#scene-modification" title="Permalink to this heading">#</a></h3>
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<p>Coming soon</p>
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</section>
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<section id="rendering-new-training-images">
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<h3>Rendering new training images<a class="headerlink" href="#rendering-new-training-images" title="Permalink to this heading">#</a></h3>
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<p>In the new dataset, training images are rendered from splats. Replace <code class="docutils literal notranslate"><span class="pre">CONFIG_LOCATION</span></code> with the location of config file saved after training.</p>
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<p><code class="docutils literal notranslate"><span class="pre">ns-nerfgs-render</span> <span class="pre">--load-config</span> <span class="pre">CONFIG_LOCATION</span> <span class="pre">--render-output-path</span> <span class="pre">exports/splatting_data</span> <span class="pre">--export-nerf-gs-data</span></code></p>
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</section>
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<section id="gsnerf-training-on-new-training-images">
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<h3>GSNeRF: Training on new training images<a class="headerlink" href="#gsnerf-training-on-new-training-images" title="Permalink to this heading">#</a></h3>
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<p><code class="docutils literal notranslate"><span class="pre">ns-train</span> <span class="pre">nerfsh</span> <span class="pre">--data</span> <span class="pre">exports/splatting_data</span> <span class="pre">--pipeline.model.camera-optimizer.mode</span> <span class="pre">off</span> <span class="pre">nerfstudio-data</span> <span class="pre">--eval-mode</span> <span class="pre">filename</span></code></p>
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</section>
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</section>
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<section id="extending-the-method">
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<h2>Extending the method<a class="headerlink" href="#extending-the-method" title="Permalink to this heading">#</a></h2>
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<p>The conversion from NeRF to GS has inefficiency as mentioned at the discussion section of the paper. We welcome your efforts to reduce the inefficiency! The code for conversion is mainly in <code class="docutils literal notranslate"><span class="pre">nerfsh/nerfsh/nerfsh_exporter.py</span></code>.</p>
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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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<p align="center">
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<img src="https://github.com/grasp-lyrl/NeRFtoGSandBack/blob/main/assets/overview.jpg">
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</p>
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<section id="nerf-sh">
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<h3>NeRF-SH<a class="headerlink" href="#nerf-sh" title="Permalink to this heading">#</a></h3>
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<p>The NeRF-SH field structure is shown above in the overview figure. NeRF-SH is modified from Nerfacto to predict spherical harmonics (degree 3 by default) for each rgb channel. The volumetric rendering process remains unchanges: at each point along a ray, we predict the spherical harmonics and calculate color based on the view direction.</p>
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</section>
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<section id="nerfgs">
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<h3>NeRFGS<a class="headerlink" href="#nerfgs" title="Permalink to this heading">#</a></h3>
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<p>Given a trained NeRF-SH, we extract pointcloud based on rendered depth, following the pointcloud export pipeline in Nerfstudio. In addition to exporting the location of each point, we export spherical harmonic coefficients and density predicted by NeRF-SH. We exclude rays with low opacity or corresponding to the sky. We initialize each Gaussian as isotropic where the scale depends on the sparsity of points in the neighborhood. Specifically, the scale of each Gaussian is half of the average distance between each point and its three nearest neighbors. To avoid large Gaussians, the scale is clipped between 0 and 0.8-th quantile of the scales in the scene. The exported Gaussian splats already captures the geometric and photometric properties of the scene. To obtain fine-grained splats and remove outliers during exportation, we finetune the splats using training views.</p>
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</section>
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<section id="gsnerf">
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<h3>GSNeRF<a class="headerlink" href="#gsnerf" title="Permalink to this heading">#</a></h3>
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<p>After editing the Gaussian splats, rendered training views from edited Gaussian splats can be used to create a new dataset. The new dataset can be used to train/update other models (especially implicit representations that are difficult to edited directly).</p>
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<div class="toc-tree">
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<ul>
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<li><a class="reference internal" href="#">From NeRFs to Gaussian Splats, and Back</a><ul>
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<li><a class="reference internal" href="#installation">Installation</a><ul>
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<li><a class="reference internal" href="#install-nerfstudio-dependencies">0. Install Nerfstudio dependencies</a></li>
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<li><a class="reference internal" href="#install-the-repository">1. Install the repository</a></li>
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<li><a class="reference internal" href="#check-installation">2. Check installation</a></li>
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</ul>
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</li>
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<li><a class="reference internal" href="#downloading-data">Downloading data</a></li>
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<li><a class="reference internal" href="#nerfgs-nerfs-to-gaussian-splats">NeRFGS: NeRFs to Gaussian Splats</a><ul>
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<li><a class="reference internal" href="#training-nerf-sh">Training NeRF-SH</a></li>
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<li><a class="reference internal" href="#nerfgs-converting-nerf-sh-to-guassian-splats">NeRFGS: Converting NeRF-SH to Guassian splats</a></li>
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<li><a class="reference internal" href="#visualize-converted-gaussian-splats">Visualize converted Gaussian splats</a></li>
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<li><a class="reference internal" href="#fintuned-nerfgs">Fintuned NeRFGS</a></li>
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</ul>
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</li>
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<li><a class="reference internal" href="#gsnerf-gaussian-splats-to-nerfs">GSNeRF: Gaussian Splats to NeRFs</a><ul>
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<li><a class="reference internal" href="#scene-modification">Scene modification</a></li>
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<li><a class="reference internal" href="#rendering-new-training-images">Rendering new training images</a></li>
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<li><a class="reference internal" href="#gsnerf-training-on-new-training-images">GSNeRF: Training on new training images</a></li>
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</ul>
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</li>
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<li><a class="reference internal" href="#extending-the-method">Extending 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="#nerf-sh">NeRF-SH</a></li>
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<li><a class="reference internal" href="#nerfgs">NeRFGS</a></li>
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<li><a class="reference internal" href="#gsnerf">GSNeRF</a></li>
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</ul>
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