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</script></nav></div><div class="container mainContainer docsContainer"><div class="wrapper"><div class="post"><header class="postHeader"></header><article><div><span><h1><a class="anchor" aria-hidden="true" id="overview"></a><a href="#overview" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>Overview</h1>
<p>PyTorch3D provides a modular differentiable renderer, but for instances where we want interactive plots or are not concerned with the differentiability of the rendering process, we provide <a href="https://github.com/facebookresearch/pytorch3d/blob/main/pytorch3d/vis/plotly_vis.py">functions to render meshes and pointclouds in plotly</a>. These plotly figures allow you to rotate and zoom the rendered images and support plotting batched data as multiple traces in a singular plot or divided into individual subplots.</p>
<h1><a class="anchor" aria-hidden="true" id="examples"></a><a href="#examples" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>Examples</h1>
<p>These rendering functions accept plotly x,y, and z axis arguments as <code>kwargs</code>, allowing us to customize the plots. Here are two plots with colored axes, a <a href="/docs/assets/plotly_pointclouds.png">Pointclouds plot</a>, a <a href="/docs/assets/plotly_meshes_batch.png">batched Meshes plot in subplots</a>, and a <a href="/docs/assets/plotly_meshes_trace.png">batched Meshes plot with multiple traces</a>. Refer to the <a href="https://pytorch3d.org/tutorials/render_textured_meshes">render textured meshes</a> and <a href="https://pytorch3d.org/tutorials/render_colored_points">render colored pointclouds</a> tutorials for code examples.</p>
<h1><a class="anchor" aria-hidden="true" id="saving-plots-to-images"></a><a href="#saving-plots-to-images" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>Saving plots to images</h1>
<p>If you want to save these plotly plots, you will need to install a separate library such as <a href="https://plotly.com/python/static-image-export/">Kaleido</a>.</p>
<p>Install Kaleido</p>
<pre><code class="hljs">$ pip <span class="hljs-keyword">install</span> Kaleido
</code></pre>
<p>Export a figure as a .png image. The image will be saved in the current working directory.</p>
<pre><code class="hljs"><span class="hljs-attribute">fig</span> = ...
fig.write_image(<span class="hljs-string">"image_name.png"</span>)
</code></pre>
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