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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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<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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<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 current current-page"><a class="current reference internal" href="#"> 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"><a class="reference internal" href="nerf2gs2nerf.html"> NeRFtoGSandBack</a></li>
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<article role="main">
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<section class="tex2jax_ignore mathjax_ignore" id="nerf">
|
||
<h1>NeRF<a class="headerlink" href="#nerf" title="Permalink to this heading">#</a></h1>
|
||
<h4>Neural Radiance Fields</h4>
|
||
<p><a class="sd-sphinx-override sd-btn sd-text-wrap sd-btn-outline-primary reference external" href="https://www.matthewtancik.com/nerf"><span>Paper Website</span></a></p>
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<section id="running-the-model">
|
||
<h2>Running the model<a class="headerlink" href="#running-the-model" title="Permalink to this heading">#</a></h2>
|
||
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>ns-train<span class="w"> </span>vanilla-nerf
|
||
</pre></div>
|
||
</div>
|
||
</section>
|
||
<section id="method-overview">
|
||
<h2>Method overview<a class="headerlink" href="#method-overview" title="Permalink to this heading">#</a></h2>
|
||
<p>If you have arrived to this site, it is likely that you have at least heard of NeRFs. This page will discuss the original NeRF paper, <em>“NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis”</em> by Mildenhall, Srinivasan, Tancik et al. (2020).</p>
|
||
<p>For most tasks, using the original NeRF model is likely not a good choice and hence we provide implementations of various other NeRF related models. It is however useful to understand how NeRF’s work as most follow ups follow a similar structure and it doesn’t require CUDA to execute (useful for stepping through the code with a debugger if you don’t have a GPU at hand).</p>
|
||
<p>The goal is to optimize a volumetric representation of a scene that can be rendered from novel viewpoints. This representation is optimized from a set of images and associated camera poses.</p>
|
||
<div class="admonition-assumptions admonition">
|
||
<p class="admonition-title">Assumptions</p>
|
||
<p>If any of the following assumptions are broken, the reconstructions may fail completely or contain artifacts such as excess geometry.</p>
|
||
<ul class="simple">
|
||
<li><p>Camera poses are known</p></li>
|
||
<li><p>Scene is static, objects do not move</p></li>
|
||
<li><p>The scene appearance is constant (ie. exposure doesn’t change)</p></li>
|
||
<li><p>Dense input capture (Each point in the scene should be visible in multiple images)</p></li>
|
||
</ul>
|
||
</div>
|
||
</section>
|
||
<section id="pipeline">
|
||
<h2>Pipeline<a class="headerlink" href="#pipeline" title="Permalink to this heading">#</a></h2>
|
||
<img alt="../../_images/models_nerf-pipeline-light.png" class="only-light align-center" src="../../_images/models_nerf-pipeline-light.png" />
|
||
<img alt="../../_images/models_nerf-pipeline-dark.png" class="only-dark align-center" src="../../_images/models_nerf-pipeline-dark.png" />
|
||
<p>Here is an overview pipeline for NeRF, we will walk through each component in this guide.</p>
|
||
<section id="field-representation">
|
||
<h3>Field representation<a class="headerlink" href="#field-representation" title="Permalink to this heading">#</a></h3>
|
||
<img alt="../../_images/models_nerf-pipeline-field-light.png" class="only-light align-center" src="../../_images/models_nerf-pipeline-field-light.png" />
|
||
<img alt="../../_images/models_nerf-pipeline-field-dark.png" class="only-dark align-center" src="../../_images/models_nerf-pipeline-field-dark.png" />
|
||
<p>NeRFs are a volumetric representation encoded into a neural network. They are not 3D meshes and they are not voxels. <strong>For each point in space the NeRF represents a view dependent radiance.</strong> More concretely each point has a density which describes how transparent or opaque a point in space is. They also have a view dependent color that changes depending on the angle the point is viewed.</p>
|
||
<a class="only-light reference internal image-reference" href="../../_images/models_nerf-field-light.png"><img alt="../../_images/models_nerf-field-light.png" class="only-light align-center" src="../../_images/models_nerf-field-light.png" style="width: 400px;" /></a>
|
||
<a class="only-dark reference internal image-reference" href="../../_images/models_nerf-field-dark.png"><img alt="../../_images/models_nerf-field-dark.png" class="only-dark align-center" src="../../_images/models_nerf-field-dark.png" style="width: 400px;" /></a>
|
||
<p>The associated NeRF fields can be instantiated with the following nerfstudio code (encoding described in next section):</p>
|
||
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">nerfstudio.fields.vanilla_nerf_field</span><span class="w"> </span><span class="kn">import</span> <span class="n">NeRFField</span>
|
||
|
||
<span class="n">field_coarse</span> <span class="o">=</span> <span class="n">NeRFField</span><span class="p">(</span><span class="n">position_encoding</span><span class="o">=</span><span class="n">pos_enc</span><span class="p">,</span> <span class="n">direction_encoding</span><span class="o">=</span><span class="n">dir_enc</span><span class="p">)</span>
|
||
<span class="n">field_fine</span> <span class="o">=</span> <span class="n">NeRFField</span><span class="p">(</span><span class="n">position_encoding</span><span class="o">=</span><span class="n">pos_enc</span><span class="p">,</span> <span class="n">direction_encoding</span><span class="o">=</span><span class="n">dir_enc</span><span class="p">)</span>
|
||
</pre></div>
|
||
</div>
|
||
<section id="positional-encoding">
|
||
<h4>Positional encoding<a class="headerlink" href="#positional-encoding" title="Permalink to this heading">#</a></h4>
|
||
<p>An extra trick is necessary to make the neural network expressive enough to represent fine details in the scene. The input coordinates <span class="math notranslate nohighlight">\((x,y,z,\theta,\phi)\)</span> need to be encoded to a higher dimensional space prior to being input into the network. You can learn more about encodings <a class="reference internal" href="../model_components/visualize_encoders.html"><span class="doc std std-doc">here</span></a>.</p>
|
||
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">nerfstudio.field_components.encodings</span><span class="w"> </span><span class="kn">import</span> <span class="n">NeRFEncoding</span>
|
||
|
||
<span class="n">pos_enc</span> <span class="o">=</span> <span class="n">NeRFEncoding</span><span class="p">(</span>
|
||
<span class="n">in_dim</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">num_frequencies</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">min_freq_exp</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">max_freq_exp</span><span class="o">=</span><span class="mf">8.0</span><span class="p">,</span> <span class="n">include_input</span><span class="o">=</span><span class="kc">True</span>
|
||
<span class="p">)</span>
|
||
<span class="n">dir_enc</span> <span class="o">=</span> <span class="n">NeRFEncoding</span><span class="p">(</span>
|
||
<span class="n">in_dim</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">num_frequencies</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">min_freq_exp</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">max_freq_exp</span><span class="o">=</span><span class="mf">4.0</span><span class="p">,</span> <span class="n">include_input</span><span class="o">=</span><span class="kc">True</span>
|
||
<span class="p">)</span>
|
||
</pre></div>
|
||
</div>
|
||
</section>
|
||
</section>
|
||
<section id="rendering">
|
||
<h3>Rendering<a class="headerlink" href="#rendering" title="Permalink to this heading">#</a></h3>
|
||
<img alt="../../_images/models_nerf-pipeline-renderer-light.png" class="only-light align-center" src="../../_images/models_nerf-pipeline-renderer-light.png" />
|
||
<img alt="../../_images/models_nerf-pipeline-renderer-dark.png" class="only-dark align-center" src="../../_images/models_nerf-pipeline-renderer-dark.png" />
|
||
<p>Now that we have a representation of space, we need some way to render new images of it. To accomplish this, we are going to <em>project</em> a ray from the target pixel and evaluate points along that ray. We then rely on classic volumetric rendering techniques <a class="reference external" href="https://dl.acm.org/doi/abs/10.1145/964965.808594">[Kajiya, 1984]</a> to composite the points into a predicted color.</p>
|
||
<p>This compositing is similar to what happens in tools like Photoshop when you layer multiple objects of varying opacity on top of each other. The only difference is that NeRF takes into account the differences in spacing between points.</p>
|
||
<p>Rending RGB images is not the only type of output render supported. It is possible to render other output types such as depth and semantics. Additional renderers can be found <a class="reference internal" href="../../reference/api/model_components/renderers.html"><span class="doc std std-doc">Here</span></a>.</p>
|
||
<p>Associated nerfstudio code:</p>
|
||
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">nerfstudio.model_components.renderers</span><span class="w"> </span><span class="kn">import</span> <span class="n">RGBRenderer</span>
|
||
|
||
<span class="n">renderer_rgb</span> <span class="o">=</span> <span class="n">RGBRenderer</span><span class="p">(</span><span class="n">background_color</span><span class="o">=</span><span class="n">colors</span><span class="o">.</span><span class="n">WHITE</span><span class="p">)</span>
|
||
<span class="c1"># Ray samples discussed in the next section</span>
|
||
<span class="n">field_outputs</span> <span class="o">=</span> <span class="n">field_coarse</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="n">ray_samples</span><span class="p">)</span>
|
||
<span class="n">weights</span> <span class="o">=</span> <span class="n">ray_samples</span><span class="o">.</span><span class="n">get_weights</span><span class="p">(</span><span class="n">field_outputs</span><span class="p">[</span><span class="n">FieldHeadNames</span><span class="o">.</span><span class="n">DENSITY</span><span class="p">])</span>
|
||
<span class="n">rgb</span> <span class="o">=</span> <span class="n">renderer_rgb</span><span class="p">(</span>
|
||
<span class="n">rgb</span><span class="o">=</span><span class="n">field_outputs</span><span class="p">[</span><span class="n">FieldHeadNames</span><span class="o">.</span><span class="n">RGB</span><span class="p">],</span>
|
||
<span class="n">weights</span><span class="o">=</span><span class="n">weights</span><span class="p">,</span>
|
||
<span class="p">)</span>
|
||
</pre></div>
|
||
</div>
|
||
<section id="sampling">
|
||
<h4>Sampling<a class="headerlink" href="#sampling" title="Permalink to this heading">#</a></h4>
|
||
<img alt="../../_images/models_nerf-pipeline-sampler-light.png" class="only-light align-center" src="../../_images/models_nerf-pipeline-sampler-light.png" />
|
||
<img alt="../../_images/models_nerf-pipeline-sampler-dark.png" class="only-dark align-center" src="../../_images/models_nerf-pipeline-sampler-dark.png" />
|
||
<p>How we sample points along rays in space is an important design decision. Various sampling strategies can be used which are discussed in detail in the <a class="reference internal" href="../model_components/visualize_samplers.html"><span class="doc std std-doc">Ray Samplers</span></a> guide. In NeRF we take advantage of a hierarchical sampling scheme that first uses a <em>uniform sampler</em> and is followed by a <em>PDF sampler</em>.</p>
|
||
<p>The uniform sampler distributes samples evenly between a predefined distance range from the camera. These are then used to compute an initial render of the scene. The renderer optionally produces <em>weights</em> for each sample that correlate with how important each sample was to the final renderer.</p>
|
||
<p>The PDF sampler uses these <em>weights</em> to generate a new set of samples that are biased to regions of higher weight. In practice, these regions are near the surface of the object.</p>
|
||
<p>Associated code:</p>
|
||
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">nerfstudio.model_components.ray_samplers</span><span class="w"> </span><span class="kn">import</span> <span class="n">PDFSampler</span><span class="p">,</span> <span class="n">UniformSampler</span>
|
||
|
||
<span class="n">sampler_uniform</span> <span class="o">=</span> <span class="n">UniformSampler</span><span class="p">(</span><span class="n">num_samples</span><span class="o">=</span><span class="n">num_coarse_samples</span><span class="p">)</span>
|
||
<span class="n">ray_samples_uniform</span> <span class="o">=</span> <span class="n">sampler_uniform</span><span class="p">(</span><span class="n">ray_bundle</span><span class="p">)</span>
|
||
|
||
<span class="n">sampler_pdf</span> <span class="o">=</span> <span class="n">PDFSampler</span><span class="p">(</span><span class="n">num_samples</span><span class="o">=</span><span class="n">num_importance_samples</span><span class="p">)</span>
|
||
<span class="n">field_outputs_coarse</span> <span class="o">=</span> <span class="n">field_coarse</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="n">ray_samples_uniform</span><span class="p">)</span>
|
||
<span class="n">weights_coarse</span> <span class="o">=</span> <span class="n">ray_samples_uniform</span><span class="o">.</span><span class="n">get_weights</span><span class="p">(</span><span class="n">field_outputs_coarse</span><span class="p">[</span><span class="n">FieldHeadNames</span><span class="o">.</span><span class="n">DENSITY</span><span class="p">])</span>
|
||
<span class="n">ray_samples_pdf</span> <span class="o">=</span> <span class="n">sampler_pdf</span><span class="p">(</span><span class="n">ray_bundle</span><span class="p">,</span> <span class="n">ray_samples_uniform</span><span class="p">,</span> <span class="n">weights_coarse</span><span class="p">)</span>
|
||
</pre></div>
|
||
</div>
|
||
<div class="admonition warning">
|
||
<p class="admonition-title">Warning</p>
|
||
<p>Described above is specific to scenes that have known bounds (ie. the Blender Synthetic dataset). For unbounded scenes, the original NeRF paper uses Normalized Device Coordinates (NDC) to warp space, along with a <em>linear in disparity</em> sampler. We do not support NDC, for unbounded scenes consider using <a class="reference internal" href="../model_components/visualize_spatial_distortions.html"><span class="doc std std-doc">Spatial Distortions</span></a>.</p>
|
||
</div>
|
||
<div class="admonition tip">
|
||
<p class="admonition-title">Tip</p>
|
||
<p>For all sampling, we use <em>Stratified</em> samples during optimization and unmodified samples during inference. Further details can be found in the <a class="reference internal" href="../model_components/visualize_samplers.html"><span class="doc std std-doc">Ray Samplers</span></a> guide.</p>
|
||
</div>
|
||
</section>
|
||
</section>
|
||
</section>
|
||
<section id="benchmarks">
|
||
<h2>Benchmarks<a class="headerlink" href="#benchmarks" title="Permalink to this heading">#</a></h2>
|
||
<section id="blender-synthetic">
|
||
<h3>Blender synthetic<a class="headerlink" href="#blender-synthetic" title="Permalink to this heading">#</a></h3>
|
||
<div class="table-wrapper colwidths-auto docutils container">
|
||
<table class="docutils align-default">
|
||
<thead>
|
||
<tr class="row-odd"><th class="head"><p>Implementation</p></th>
|
||
<th class="head text-center"><p>Mic</p></th>
|
||
<th class="head"><p>Ficus</p></th>
|
||
<th class="head text-center"><p>Chair</p></th>
|
||
<th class="head"><p>Hotdog</p></th>
|
||
<th class="head"><p>Materials</p></th>
|
||
<th class="head"><p>Drums</p></th>
|
||
<th class="head"><p>Ship</p></th>
|
||
<th class="head"><p>Lego</p></th>
|
||
<th class="head"><p>Average</p></th>
|
||
</tr>
|
||
</thead>
|
||
<tbody>
|
||
<tr class="row-even"><td><p>nerfstudio</p></td>
|
||
<td class="text-center"><p>33.76</p></td>
|
||
<td><p><strong>31.98</strong></p></td>
|
||
<td class="text-center"><p><strong>34.35</strong></p></td>
|
||
<td><p>36.57</p></td>
|
||
<td><p><strong>31.00</strong></p></td>
|
||
<td><p><strong>25.11</strong></p></td>
|
||
<td><p>29.87</p></td>
|
||
<td><p><strong>34.46</strong></p></td>
|
||
<td><p><strong>32.14</strong></p></td>
|
||
</tr>
|
||
<tr class="row-odd"><td><p><a class="reference external" href="https://github.com/bmild/nerf">TF NeRF</a></p></td>
|
||
<td class="text-center"><p>32.91</p></td>
|
||
<td><p>30.13</p></td>
|
||
<td class="text-center"><p>33.00</p></td>
|
||
<td><p>36.18</p></td>
|
||
<td><p>29.62</p></td>
|
||
<td><p>25.01</p></td>
|
||
<td><p>28.65</p></td>
|
||
<td><p>32.54</p></td>
|
||
<td><p>31.04</p></td>
|
||
</tr>
|
||
<tr class="row-even"><td><p><a class="reference external" href="https://github.com/google-research/google-research/tree/master/jaxnerf">JaxNeRF</a></p></td>
|
||
<td class="text-center"><p><strong>34.53</strong></p></td>
|
||
<td><p>30.43</p></td>
|
||
<td class="text-center"><p>34.08</p></td>
|
||
<td><p><strong>36.92</strong></p></td>
|
||
<td><p>29.91</p></td>
|
||
<td><p>25.03</p></td>
|
||
<td><p><strong>29.36</strong></p></td>
|
||
<td><p>33.28</p></td>
|
||
<td><p>31.69</p></td>
|
||
</tr>
|
||
</tbody>
|
||
</table>
|
||
</div>
|
||
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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="#">NeRF</a><ul>
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<li><a class="reference internal" href="#running-the-model">Running the model</a></li>
|
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<li><a class="reference internal" href="#method-overview">Method overview</a></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="#field-representation">Field representation</a><ul>
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</ul>
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<li><a class="reference internal" href="#rendering">Rendering</a><ul>
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<li><a class="reference internal" href="#sampling">Sampling</a></li>
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</ul>
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</li>
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</li>
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<li><a class="reference internal" href="#benchmarks">Benchmarks</a><ul>
|
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<li><a class="reference internal" href="#blender-synthetic">Blender synthetic</a></li>
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