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<li class="toctree-l2"><a class="reference internal" href="../../reference/cli/ns_eval.html">ns-eval</a></li>
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<article role="main">
<section class="tex2jax_ignore mathjax_ignore" id="lerf">
<h1>LERF<a class="headerlink" href="#lerf" title="Permalink to this heading">#</a></h1>
<h4>📎 Language Embedded Radiance Fields 🚜</h4>
<p><a class="sd-sphinx-override sd-btn sd-text-wrap sd-btn-outline-primary reference external" href="https://www.lerf.io/"><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/kerrj/lerf"><span>Code</span></a></p>
<video id="teaser" muted autoplay playsinline loop controls width="100%">
<source id="mp4" src="https://www.lerf.io/data/teaser.mp4" type="video/mp4">
</video>
<p><strong>Grounding CLIP vectors volumetrically inside NeRF allows flexible natural language queries in 3D</strong></p>
<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/kerrj/lerf
</pre></div>
</div>
</section>
<section id="running-lerf">
<h2>Running LERF<a class="headerlink" href="#running-lerf" title="Permalink to this heading">#</a></h2>
<p>Details for running LERF (built with Nerfstudio!) can be found <a class="reference external" href="https://github.com/kerrj/lerf">here</a>. Once installed, run:</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>ns-train<span class="w"> </span>lerf<span class="w"> </span>--help
</pre></div>
</div>
<p>Three variants of LERF are provided:</p>
<div class="table-wrapper colwidths-auto docutils container">
<table class="docutils align-default">
<thead>
<tr class="row-odd"><th class="head"><p>Method</p></th>
<th class="head"><p>Description</p></th>
<th class="head"><p>Memory</p></th>
<th class="head"><p>Quality</p></th>
</tr>
</thead>
<tbody>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">lerf-big</span></code></p></td>
<td><p>LERF with OpenCLIP ViT-L/14</p></td>
<td><p>~22 GB</p></td>
<td><p>Best</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">lerf</span></code></p></td>
<td><p>Model with OpenCLIP ViT-B/16, used in paper</p></td>
<td><p>~15 GB</p></td>
<td><p>Good</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">lerf-lite</span></code></p></td>
<td><p>LERF with smaller network and less LERF samples</p></td>
<td><p>~8 GB</p></td>
<td><p>Ok</p></td>
</tr>
</tbody>
</table>
</div>
<p><code class="docutils literal notranslate"><span class="pre">lerf-lite</span></code> should work on a single NVIDIA 2080.
<code class="docutils literal notranslate"><span class="pre">lerf-big</span></code> is experimental, and needs further tuning.</p>
</section>
<section id="method">
<h2>Method<a class="headerlink" href="#method" title="Permalink to this heading">#</a></h2>
<p>LERF enables pixel-aligned queries of the distilled 3D CLIP embeddings without relying on region proposals, masks, or fine-tuning, supporting long-tail open-vocabulary queries hierarchically across the volume.</p>
<section id="multi-scale-supervision">
<h3>Multi-scale supervision<a class="headerlink" href="#multi-scale-supervision" title="Permalink to this heading">#</a></h3>
<p>To supervise language embeddings, we pre-compute an image pyramid of CLIP features for each training view. Then, each sampled ray during optimization is supervised by interpolating the CLIP embedding within this pyramid.</p>
<a class="reference internal image-reference" href="https://www.lerf.io/data/clip_features.png"><img alt="https://www.lerf.io/data/clip_features.png" src="https://www.lerf.io/data/clip_features.png" style="width: 100%;" /></a>
</section>
<section id="lerf-optimization">
<h3>LERF Optimization<a class="headerlink" href="#lerf-optimization" title="Permalink to this heading">#</a></h3>
<p>LERF optimizes a dense, multi-scale language 3D field by volume rendering CLIP embeddings along training rays, supervising these embeddings with multi-scale CLIP features across multi-view training images.</p>
<p>Inspired by Distilled Feature Fields (DFF), we use DINO features to regularize CLIP features. This leads to qualitative improvements in object boundaries, as CLIP embeddings in 3D can be sensitive to floaters and regions with few views.</p>
<p>After optimization, LERF can extract 3D relevancy maps for language queries interactively in real-time.</p>
<a class="reference internal image-reference" href="https://www.lerf.io/data/nerf_render.png"><img alt="https://www.lerf.io/data/nerf_render.png" src="https://www.lerf.io/data/nerf_render.png" style="width: 100%;" /></a>
</section>
<section id="visualizing-relevancy-for-text-queries">
<h3>Visualizing relevancy for text queries<a class="headerlink" href="#visualizing-relevancy-for-text-queries" title="Permalink to this heading">#</a></h3>
<p>Set the “Output Render” type to <code class="docutils literal notranslate"><span class="pre">relevancy_0</span></code>, and enter the text query in the “LERF Positives” textbox (see image). The output render will show the 3D relevancy map for the query. View the <a class="reference external" href="https://lerf.io">project page</a> for more examples and details about the relevancy map normalization.</p>
<center>
<img id="lerf_viewer" src="https://www.lerf.io/data/lerf_screen.png" width="40%">
</center>
</section>
</section>
<section id="results">
<h2>Results<a class="headerlink" href="#results" title="Permalink to this heading">#</a></h2>
<p>For results, view the <a class="reference external" href="https://lerf.io">project page</a>!</p>
<p>Datasets used in the original paper can be found <a class="reference external" href="https://drive.google.com/drive/folders/1LUzwEvBCE19PNYcwfmrG-9FLpZLbi4on?usp=sharing">here</a>.</p>
<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>@article{lerf2023,
author = {Kerr, Justin and Kim, Chung Min and Goldberg, Ken and Kanazawa, Angjoo and Tancik, Matthew},
title = {LERF: Language Embedded Radiance Fields},
journal = {arXiv preprint arXiv:2303.09553},
year = {2023},
}
</pre></div>
</div>
</section>
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<ul>
<li><a class="reference internal" href="#">LERF</a><ul>
<li><a class="reference internal" href="#installation">Installation</a></li>
<li><a class="reference internal" href="#running-lerf">Running LERF</a></li>
<li><a class="reference internal" href="#method">Method</a><ul>
<li><a class="reference internal" href="#multi-scale-supervision">Multi-scale supervision</a></li>
<li><a class="reference internal" href="#lerf-optimization">LERF Optimization</a></li>
<li><a class="reference internal" href="#visualizing-relevancy-for-text-queries">Visualizing relevancy for text queries</a></li>
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