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<article role="main">
<section class="tex2jax_ignore mathjax_ignore" id="bionerf">
<h1>BioNeRF<a class="headerlink" href="#bionerf" title="Permalink to this heading">#</a></h1>
<h4> Biologically Plausible Neural Radiance Fields for View Synthesis</h4>
<p><a class="sd-sphinx-override sd-btn sd-text-wrap sd-btn-outline-primary reference external" href="https://leandropassosjr.github.io/BioNeRF/"><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/Leandropassosjr/BioNeRF"><span>Official Code</span></a></p>
<p><a class="sd-sphinx-override sd-btn sd-text-wrap sd-btn-outline-primary reference external" href="https://github.com/Leandropassosjr/ns_bionerf"><span>Nerfstudio add-on code</span></a></p>
<p><img alt="examples" src="https://leandropassosjr.github.io/BioNeRF/static/images/gifs.gif" /><br></p>
<p><strong>Biologically Plausible Neural Radiance Fields</strong></p>
<section id="installation">
<h2>Installation<a class="headerlink" href="#installation" title="Permalink to this heading">#</a></h2>
<p>Install nerfstudio dependencies.</p>
</section>
<section id="running-bionerf">
<h2>Running BioNeRF<a class="headerlink" href="#running-bionerf" title="Permalink to this heading">#</a></h2>
<p>Once the nerfstudio dependencies are installed, run:</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>ns-train<span class="w"> </span>bionerf<span class="w"> </span>--help
</pre></div>
</div>
</section>
<section id="method">
<h2>Method<a class="headerlink" href="#method" title="Permalink to this heading">#</a></h2>
<section id="overview">
<h3>Overview<a class="headerlink" href="#overview" title="Permalink to this heading">#</a></h3>
<p><a class="reference external" href="https://arxiv.org/pdf/2402.07310.pdf">BioNeRF</a> (Biologically Plausible Neural Radiance Fields) extends <a class="reference external" href="http://www.matthewtancik.com/nerf">NeRF</a> by implementing a cognitive-inspired mechanism that fuses inputs from multiple sources into a memory-like structure, thus improving the storing capacity and extracting more intrinsic and correlated information. BioNeRF also mimics a behavior observed in pyramidal cells concerning contextual information, in which the memory is provided as the context and combined with the inputs of two subsequent blocks of dense layers, one responsible for producing the volumetric densities and the other the colors used to render the novel view.</p>
</section>
</section>
<section id="pipeline">
<h2>Pipeline<a class="headerlink" href="#pipeline" title="Permalink to this heading">#</a></h2>
<p><img alt="pipeline" src="https://leandropassosjr.github.io/BioNeRF/static/images/BioNeRF.png" /><br></p>
<p>Here is an overview pipeline for BioNeRF, we will walk through each component in this guide.</p>
<section id="positional-feature-extraction">
<h3>Positional Feature Extraction<a class="headerlink" href="#positional-feature-extraction" title="Permalink to this heading">#</a></h3>
<p>The first step consists of feeding two neural models simultaneously, namely <span class="math notranslate nohighlight">\(M_{\Delta}\)</span> and <span class="math notranslate nohighlight">\(M_c\)</span>, with the camera positional information. The output of these models encodes the positional information from the input image. Although the input is the same, the neural models do not share weights and follow a different flow in the next steps.</p>
</section>
<section id="cognitive-filtering">
<h3>Cognitive Filtering<a class="headerlink" href="#cognitive-filtering" title="Permalink to this heading">#</a></h3>
<p>This step performs a series of operations, called <em>filters</em>, that work on the embeddings coming from the previous step. There are four filters this step derives: density, color, memory, and modulation.</p>
</section>
<section id="memory-updating">
<h3>Memory Updating<a class="headerlink" href="#memory-updating" title="Permalink to this heading">#</a></h3>
<p>Updating the memory requires the implementation of a mechanism capable of obliterating trivial information, which is performed using the memory filter (Step 3.1 in the figure). Fist, one needs to compute a signal modulation <strong><span class="math notranslate nohighlight">\(\mu\)</span></strong>, for further introducing new experiences in the memory <strong><span class="math notranslate nohighlight">\(\Psi\)</span></strong> through the modulating variable <strong><span class="math notranslate nohighlight">\(\mu\)</span></strong> using a <span class="math notranslate nohighlight">\(\textit{tanh}\)</span> function (Step 3.2 in the figure).</p>
</section>
<section id="contextual-inference">
<h3>Contextual Inference<a class="headerlink" href="#contextual-inference" title="Permalink to this heading">#</a></h3>
<p>This step is responsible for adding contextual information to BioNeRF. Two new embeddings are generated, i.e., <strong><span class="math notranslate nohighlight">\({h}^{\prime}_\Delta\)</span></strong> and <strong><span class="math notranslate nohighlight">\({h}^{\prime}_c\)</span></strong> based on density and color filters, respectively (Step 4 in the figure), which further feed two neural models, i.e., <span class="math notranslate nohighlight">\(M^\prime_\Delta\)</span> and <span class="math notranslate nohighlight">\(M^\prime_c\)</span>. Subsequently, <span class="math notranslate nohighlight">\(M^\prime_\Delta\)</span> outputs the volume density, while color information is predicted by <span class="math notranslate nohighlight">\(M^\prime_c\)</span>, further used to compute the final predicted pixel information and the loss function.</p>
</section>
</section>
<section id="results">
<h2>Results<a class="headerlink" href="#results" title="Permalink to this heading">#</a></h2>
<p><strong>Blender</strong> (synthetic)</p>
<div class="table-wrapper colwidths-auto docutils container">
<table class="docutils align-default">
<thead>
<tr class="row-odd"><th class="head"><p></p></th>
<th class="head"><p>drums</p></th>
<th class="head"><p>materials</p></th>
<th class="head"><p>ficus</p></th>
<th class="head"><p>ship</p></th>
<th class="head"><p>mic</p></th>
<th class="head"><p>chair</p></th>
<th class="head"><p>lego</p></th>
<th class="head"><p>hotdog</p></th>
<th class="head"><p>AVG</p></th>
</tr>
</thead>
<tbody>
<tr class="row-even"><td><p>PSNR</p></td>
<td><p>25.66</p></td>
<td><p>29.74</p></td>
<td><p>29.56</p></td>
<td><p>29.57</p></td>
<td><p>33.38</p></td>
<td><p>34.63</p></td>
<td><p>31.82</p></td>
<td><p>37.23</p></td>
<td><p>31.45</p></td>
</tr>
<tr class="row-odd"><td><p>SSIM</p></td>
<td><p>0.927</p></td>
<td><p>0.957</p></td>
<td><p>0.965</p></td>
<td><p>0.874</p></td>
<td><p>0.978</p></td>
<td><p>0.977</p></td>
<td><p>0.963</p></td>
<td><p>0.980</p></td>
<td><p>0.953</p></td>
</tr>
<tr class="row-even"><td><p>LPIPS</p></td>
<td><p>0.047</p></td>
<td><p>0.018</p></td>
<td><p>0.017</p></td>
<td><p>0.068</p></td>
<td><p>0.018</p></td>
<td><p>0.011</p></td>
<td><p>0.016</p></td>
<td><p>0.010</p></td>
<td><p>0.026</p></td>
</tr>
</tbody>
</table>
</div>
<img alt="https://leandropassosjr.github.io/BioNeRF/static/images/blender.jpeg" src="https://leandropassosjr.github.io/BioNeRF/static/images/blender.jpeg" />
<p>Ground-truth (top) and synthetic view (bottom) images generated by BioNeRF regarding four Realistic Blender dataset’s scenes.</p>
<p><strong>LLFF</strong> (real)</p>
<div class="table-wrapper colwidths-auto docutils container">
<table class="docutils align-default">
<thead>
<tr class="row-odd"><th class="head"><p></p></th>
<th class="head"><p>Fern</p></th>
<th class="head"><p>Flower</p></th>
<th class="head"><p>Fortress</p></th>
<th class="head"><p>Horns</p></th>
<th class="head"><p>Leaves</p></th>
<th class="head"><p>Orchids</p></th>
<th class="head"><p>Room</p></th>
<th class="head"><p>T-Rex</p></th>
<th class="head"><p>AVG</p></th>
</tr>
</thead>
<tbody>
<tr class="row-even"><td><p>PSNR</p></td>
<td><p>25.17</p></td>
<td><p>27.89</p></td>
<td><p>32.34</p></td>
<td><p>27.99</p></td>
<td><p>22.23</p></td>
<td><p>20.80</p></td>
<td><p>30.75</p></td>
<td><p>27.56</p></td>
<td><p>27.01</p></td>
</tr>
<tr class="row-odd"><td><p>SSIM</p></td>
<td><p>0.837</p></td>
<td><p>0.873</p></td>
<td><p>0.914</p></td>
<td><p>0.882</p></td>
<td><p>0.796</p></td>
<td><p>0.714</p></td>
<td><p>0.911</p></td>
<td><p>0.911</p></td>
<td><p>0.861</p></td>
</tr>
<tr class="row-even"><td><p>LPIPS</p></td>
<td><p>0.093</p></td>
<td><p>0.055</p></td>
<td><p>0.025</p></td>
<td><p>0.070</p></td>
<td><p>0.103</p></td>
<td><p>0.122</p></td>
<td><p>0.029</p></td>
<td><p>0.044</p></td>
<td><p>0.068</p></td>
</tr>
</tbody>
</table>
</div>
<img alt="https://leandropassosjr.github.io/BioNeRF/static/images/llff.jpeg" src="https://leandropassosjr.github.io/BioNeRF/static/images/llff.jpeg" />
<p>Ground-truth (top) and synthetic view (bottom) images generated by BioNeRF regarding four LLFF dataset’s scenes.</p>
</section>
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<ul>
<li><a class="reference internal" href="#">BioNeRF</a><ul>
<li><a class="reference internal" href="#installation">Installation</a></li>
<li><a class="reference internal" href="#running-bionerf">Running BioNeRF</a></li>
<li><a class="reference internal" href="#method">Method</a><ul>
<li><a class="reference internal" href="#overview">Overview</a></li>
</ul>
</li>
<li><a class="reference internal" href="#pipeline">Pipeline</a><ul>
<li><a class="reference internal" href="#positional-feature-extraction">Positional Feature Extraction</a></li>
<li><a class="reference internal" href="#cognitive-filtering">Cognitive Filtering</a></li>
<li><a class="reference internal" href="#memory-updating">Memory Updating</a></li>
<li><a class="reference internal" href="#contextual-inference">Contextual Inference</a></li>
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<li><a class="reference internal" href="#results">Results</a></li>
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