update for version 0.5.0

2025-12-21 14:50:36 +08:00 · 2021-08-10 07:51:01 -07:00
parent 13e2fd2b5b
commit 3ccabda50d
65 changed files with 1660 additions and 1150 deletions
--- a/tutorials/render_textured_meshes.html
+++ b/tutorials/render_textured_meshes.html
@@ -1,4 +1,4 @@
-<!DOCTYPE html><html lang=""><head><meta charSet="utf-8"/><meta http-equiv="X-UA-Compatible" content="IE=edge"/><title>PyTorch3D · A library for deep learning with 3D data</title><meta name="viewport" content="width=device-width"/><meta name="generator" content="Docusaurus"/><meta name="description" content="A library for deep learning with 3D data"/><meta property="og:title" content="PyTorch3D · A library for deep learning with 3D data"/><meta property="og:type" content="website"/><meta property="og:url" content="https://pytorch3d.org/"/><meta property="og:description" content="A library for deep learning with 3D data"/><meta property="og:image" content="https://pytorch3d.org/img/pytorch3dlogoicon.svg"/><meta name="twitter:card" content="summary"/><meta name="twitter:image" content="https://pytorch3d.org/img/pytorch3dlogoicon.svg"/><link rel="shortcut icon" href="/img/pytorch3dfavicon.png"/><link rel="stylesheet" href="//cdnjs.cloudflare.com/ajax/libs/highlight.js/9.12.0/styles/default.min.css"/><script>
+<!DOCTYPE html><html lang=""><head><meta charSet="utf-8"/><meta http-equiv="X-UA-Compatible" content="IE=edge"/><title>PyTorch3D · A library for deep learning with 3D data</title><meta name="viewport" content="width=device-width, initial-scale=1.0"/><meta name="generator" content="Docusaurus"/><meta name="description" content="A library for deep learning with 3D data"/><meta property="og:title" content="PyTorch3D · A library for deep learning with 3D data"/><meta property="og:type" content="website"/><meta property="og:url" content="https://pytorch3d.org/"/><meta property="og:description" content="A library for deep learning with 3D data"/><meta property="og:image" content="https://pytorch3d.org/img/pytorch3dlogoicon.svg"/><meta name="twitter:card" content="summary"/><meta name="twitter:image" content="https://pytorch3d.org/img/pytorch3dlogoicon.svg"/><link rel="shortcut icon" href="/img/pytorch3dfavicon.png"/><link rel="stylesheet" href="//cdnjs.cloudflare.com/ajax/libs/highlight.js/9.12.0/styles/default.min.css"/><script>
              (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
              (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new Date();a=s.createElement(o),
              m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)
@@ -82,7 +82,8 @@
 </div>
 </div>
 <div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
-</div><div class="inner_cell">
+</div>
+<div class="inner_cell">
 <div class="text_cell_render border-box-sizing rendered_html">
 <h1 id="Render-a-textured-mesh">Render a textured mesh<a class="anchor-link" href="#Render-a-textured-mesh">¶</a></h1><p>This tutorial shows how to:</p>
 <ul>
@@ -96,16 +97,18 @@
 </div>
 </div>
 <div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
-</div><div class="inner_cell">
+</div>
+<div class="inner_cell">
 <div class="text_cell_render border-box-sizing rendered_html">
 <h2 id="0.-Install-and-Import-modules">0. Install and Import modules<a class="anchor-link" href="#0.-Install-and-Import-modules">¶</a></h2>
 </div>
 </div>
 </div>
 <div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
-</div><div class="inner_cell">
+</div>
+<div class="inner_cell">
 <div class="text_cell_render border-box-sizing rendered_html">
-<p>If <code>torch</code>, <code>torchvision</code> and <code>pytorch3d</code> are not installed, run the following cell:</p>
+<p>Ensure <code>torch</code> and <code>torchvision</code> are installed. If <code>pytorch3d</code> is not installed, install it using the following cell:</p>
 </div>
 </div>
 </div>
@@ -114,19 +117,25 @@
 <div class="prompt input_prompt">In [ ]:</div>
 <div class="inner_cell">
 <div class="input_area">
-<div class="highlight hl-ipython3"><pre><span></span><span class="o">!</span>pip install torch torchvision
-<span class="kn">import</span> <span class="nn">os</span>
+<div class="highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
 <span class="kn">import</span> <span class="nn">sys</span>
 <span class="kn">import</span> <span class="nn">torch</span>
-<span class="k">if</span> <span class="n">torch</span><span class="o">.</span><span class="n">__version__</span><span class="o">==</span><span class="s1">'1.6.0+cu101'</span> <span class="ow">and</span> <span class="n">sys</span><span class="o">.</span><span class="n">platform</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s1">'linux'</span><span class="p">):</span>
-    <span class="o">!</span>pip install pytorch3d
-<span class="k">else</span><span class="p">:</span>
-    <span class="n">need_pytorch3d</span><span class="o">=</span><span class="kc">False</span>
-    <span class="k">try</span><span class="p">:</span>
-        <span class="kn">import</span> <span class="nn">pytorch3d</span>
-    <span class="k">except</span> <span class="n">ModuleNotFoundError</span><span class="p">:</span>
-        <span class="n">need_pytorch3d</span><span class="o">=</span><span class="kc">True</span>
-    <span class="k">if</span> <span class="n">need_pytorch3d</span><span class="p">:</span>
+<span class="n">need_pytorch3d</span><span class="o">=</span><span class="kc">False</span>
+<span class="k">try</span><span class="p">:</span>
+    <span class="kn">import</span> <span class="nn">pytorch3d</span>
+<span class="k">except</span> <span class="ne">ModuleNotFoundError</span><span class="p">:</span>
+    <span class="n">need_pytorch3d</span><span class="o">=</span><span class="kc">True</span>
+<span class="k">if</span> <span class="n">need_pytorch3d</span><span class="p">:</span>
+    <span class="k">if</span> <span class="n">torch</span><span class="o">.</span><span class="n">__version__</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s2">"1.9"</span><span class="p">)</span> <span class="ow">and</span> <span class="n">sys</span><span class="o">.</span><span class="n">platform</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s2">"linux"</span><span class="p">):</span>
+        <span class="c1"># We try to install PyTorch3D via a released wheel.</span>
+        <span class="n">version_str</span><span class="o">=</span><span class="s2">""</span><span class="o">.</span><span class="n">join</span><span class="p">([</span>
+            <span class="sa">f</span><span class="s2">"py3</span><span class="si">{</span><span class="n">sys</span><span class="o">.</span><span class="n">version_info</span><span class="o">.</span><span class="n">minor</span><span class="si">}</span><span class="s2">_cu"</span><span class="p">,</span>
+            <span class="n">torch</span><span class="o">.</span><span class="n">version</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s2">"."</span><span class="p">,</span><span class="s2">""</span><span class="p">),</span>
+            <span class="sa">f</span><span class="s2">"_pyt</span><span class="si">{</span><span class="n">torch</span><span class="o">.</span><span class="n">__version__</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="mi">5</span><span class="p">:</span><span class="mi">2</span><span class="p">]</span><span class="si">}</span><span class="s2">"</span>
+        <span class="p">])</span>
+        <span class="o">!</span>pip install pytorch3d -f https://dl.fbaipublicfiles.com/pytorch3d/packaging/wheels/<span class="o">{</span>version_str<span class="o">}</span>/download.html
+    <span class="k">else</span><span class="p">:</span>
+        <span class="c1"># We try to install PyTorch3D from source.</span>
        <span class="o">!</span>curl -LO https://github.com/NVIDIA/cub/archive/1.10.0.tar.gz
        <span class="o">!</span>tar xzf <span class="m">1</span>.10.0.tar.gz
        <span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s2">"CUB_HOME"</span><span class="p">]</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">getcwd</span><span class="p">()</span> <span class="o">+</span> <span class="s2">"/cub-1.10.0"</span>
@@ -144,16 +153,15 @@
 <div class="highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
 <span class="kn">import</span> <span class="nn">torch</span>
 <span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-<span class="kn">from</span> <span class="nn">skimage.io</span> <span class="k">import</span> <span class="n">imread</span>

 <span class="c1"># Util function for loading meshes</span>
-<span class="kn">from</span> <span class="nn">pytorch3d.io</span> <span class="k">import</span> <span class="n">load_objs_as_meshes</span><span class="p">,</span> <span class="n">load_obj</span>
+<span class="kn">from</span> <span class="nn">pytorch3d.io</span> <span class="kn">import</span> <span class="n">load_objs_as_meshes</span><span class="p">,</span> <span class="n">load_obj</span>

 <span class="c1"># Data structures and functions for rendering</span>
-<span class="kn">from</span> <span class="nn">pytorch3d.structures</span> <span class="k">import</span> <span class="n">Meshes</span>
-<span class="kn">from</span> <span class="nn">pytorch3d.vis.plotly_vis</span> <span class="k">import</span> <span class="n">AxisArgs</span><span class="p">,</span> <span class="n">plot_batch_individually</span><span class="p">,</span> <span class="n">plot_scene</span>
-<span class="kn">from</span> <span class="nn">pytorch3d.vis.texture_vis</span> <span class="k">import</span> <span class="n">texturesuv_image_matplotlib</span>
-<span class="kn">from</span> <span class="nn">pytorch3d.renderer</span> <span class="k">import</span> <span class="p">(</span>
+<span class="kn">from</span> <span class="nn">pytorch3d.structures</span> <span class="kn">import</span> <span class="n">Meshes</span>
+<span class="kn">from</span> <span class="nn">pytorch3d.vis.plotly_vis</span> <span class="kn">import</span> <span class="n">AxisArgs</span><span class="p">,</span> <span class="n">plot_batch_individually</span><span class="p">,</span> <span class="n">plot_scene</span>
+<span class="kn">from</span> <span class="nn">pytorch3d.vis.texture_vis</span> <span class="kn">import</span> <span class="n">texturesuv_image_matplotlib</span>
+<span class="kn">from</span> <span class="nn">pytorch3d.renderer</span> <span class="kn">import</span> <span class="p">(</span>
    <span class="n">look_at_view_transform</span><span class="p">,</span>
    <span class="n">FoVPerspectiveCameras</span><span class="p">,</span> 
    <span class="n">PointLights</span><span class="p">,</span> 
@@ -177,7 +185,8 @@
 </div>
 </div>
 <div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
-</div><div class="inner_cell">
+</div>
+<div class="inner_cell">
 <div class="text_cell_render border-box-sizing rendered_html">
 <p>If using <strong>Google Colab</strong>, fetch the utils file for plotting image grids:</p>
 </div>
@@ -189,14 +198,15 @@
 <div class="inner_cell">
 <div class="input_area">
 <div class="highlight hl-ipython3"><pre><span></span><span class="o">!</span>wget https://raw.githubusercontent.com/facebookresearch/pytorch3d/master/docs/tutorials/utils/plot_image_grid.py
-<span class="kn">from</span> <span class="nn">plot_image_grid</span> <span class="k">import</span> <span class="n">image_grid</span>
+<span class="kn">from</span> <span class="nn">plot_image_grid</span> <span class="kn">import</span> <span class="n">image_grid</span>
 </pre></div>
 </div>
 </div>
 </div>
 </div>
 <div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
-</div><div class="inner_cell">
+</div>
+<div class="inner_cell">
 <div class="text_cell_render border-box-sizing rendered_html">
 <p>OR if running <strong>locally</strong> uncomment and run the following cell:</p>
 </div>
@@ -214,17 +224,19 @@
 </div>
 </div>
 <div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
-</div><div class="inner_cell">
+</div>
+<div class="inner_cell">
 <div class="text_cell_render border-box-sizing rendered_html">
-<h3 id="1.-Load-a-mesh-and-texture-file">1. Load a mesh and texture file<a class="anchor-link" href="#1.-Load-a-mesh-and-texture-file">¶</a></h3><p>Load an <code>.obj</code> file and it's associated <code>.mtl</code> file and create a <strong>Textures</strong> and <strong>Meshes</strong> object.</p>
+<h3 id="1.-Load-a-mesh-and-texture-file">1. Load a mesh and texture file<a class="anchor-link" href="#1.-Load-a-mesh-and-texture-file">¶</a></h3><p>Load an <code>.obj</code> file and its associated <code>.mtl</code> file and create a <strong>Textures</strong> and <strong>Meshes</strong> object.</p>
 <p><strong>Meshes</strong> is a unique datastructure provided in PyTorch3D for working with batches of meshes of different sizes.</p>
-<p><strong>TexturesUV</strong> is an auxillary datastructure for storing vertex uv and texture maps for meshes.</p>
+<p><strong>TexturesUV</strong> is an auxiliary datastructure for storing vertex uv and texture maps for meshes.</p>
 <p><strong>Meshes</strong> has several class methods which are used throughout the rendering pipeline.</p>
 </div>
 </div>
 </div>
 <div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
-</div><div class="inner_cell">
+</div>
+<div class="inner_cell">
 <div class="text_cell_render border-box-sizing rendered_html">
 <p>If running this notebook using <strong>Google Colab</strong>, run the following cell to fetch the mesh obj and texture files and save it at the path <code>data/cow_mesh</code>:
 If running locally, the data is already available at the correct path.</p>
@@ -269,7 +281,8 @@ If running locally, the data is already available at the correct path.</p>
 </div>
 </div>
 <div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
-</div><div class="inner_cell">
+</div>
+<div class="inner_cell">
 <div class="text_cell_render border-box-sizing rendered_html">
 <h4 id="Let's-visualize-the-texture-map">Let's visualize the texture map<a class="anchor-link" href="#Let's-visualize-the-texture-map">¶</a></h4>
 </div>
@@ -283,7 +296,6 @@ If running locally, the data is already available at the correct path.</p>
 <div class="highlight hl-ipython3"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">7</span><span class="p">,</span><span class="mi">7</span><span class="p">))</span>
 <span class="n">texture_image</span><span class="o">=</span><span class="n">mesh</span><span class="o">.</span><span class="n">textures</span><span class="o">.</span><span class="n">maps_padded</span><span class="p">()</span>
 <span class="n">plt</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">texture_image</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="s2">"off"</span><span class="p">);</span>
 <span class="n">plt</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s2">"off"</span><span class="p">);</span>
 </pre></div>
 </div>
@@ -291,7 +303,8 @@ If running locally, the data is already available at the correct path.</p>
 </div>
 </div>
 <div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
-</div><div class="inner_cell">
+</div>
+<div class="inner_cell">
 <div class="text_cell_render border-box-sizing rendered_html">
 <p>PyTorch3D has a built-in way to view the texture map with matplotlib along with the points on the map corresponding to vertices. There is also a method, texturesuv_image_PIL, to get a similar image which can be saved to a file.</p>
 </div>
@@ -304,7 +317,6 @@ If running locally, the data is already available at the correct path.</p>
 <div class="input_area">
 <div class="highlight hl-ipython3"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">7</span><span class="p">,</span><span class="mi">7</span><span class="p">))</span>
 <span class="n">texturesuv_image_matplotlib</span><span class="p">(</span><span class="n">mesh</span><span class="o">.</span><span class="n">textures</span><span class="p">,</span> <span class="n">subsample</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="s2">"off"</span><span class="p">);</span>
 <span class="n">plt</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s2">"off"</span><span class="p">);</span>
 </pre></div>
 </div>
@@ -312,10 +324,11 @@ If running locally, the data is already available at the correct path.</p>
 </div>
 </div>
 <div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
-</div><div class="inner_cell">
+</div>
+<div class="inner_cell">
 <div class="text_cell_render border-box-sizing rendered_html">
 <h2 id="2.-Create-a-renderer">2. Create a renderer<a class="anchor-link" href="#2.-Create-a-renderer">¶</a></h2><p>A renderer in PyTorch3D is composed of a <strong>rasterizer</strong> and a <strong>shader</strong> which each have a number of subcomponents such as a <strong>camera</strong> (orthographic/perspective). Here we initialize some of these components and use default values for the rest.</p>
-<p>In this example we will first create a <strong>renderer</strong> which uses a <strong>perspective camera</strong>, a <strong>point light</strong> and applies <strong>phong shading</strong>. Then we learn how to vary different components using the modular API.</p>
+<p>In this example we will first create a <strong>renderer</strong> which uses a <strong>perspective camera</strong>, a <strong>point light</strong> and applies <strong>Phong shading</strong>. Then we learn how to vary different components using the modular API.</p>
 </div>
 </div>
 </div>
@@ -346,7 +359,7 @@ If running locally, the data is already available at the correct path.</p>
 <span class="c1"># -z direction. </span>
 <span class="n">lights</span> <span class="o">=</span> <span class="n">PointLights</span><span class="p">(</span><span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">,</span> <span class="n">location</span><span class="o">=</span><span class="p">[[</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="o">-</span><span class="mf">3.0</span><span class="p">]])</span>

-<span class="c1"># Create a phong renderer by composing a rasterizer and a shader. The textured phong shader will </span>
+<span class="c1"># Create a Phong renderer by composing a rasterizer and a shader. The textured Phong shader will </span>
 <span class="c1"># interpolate the texture uv coordinates for each vertex, sample from a texture image and </span>
 <span class="c1"># apply the Phong lighting model</span>
 <span class="n">renderer</span> <span class="o">=</span> <span class="n">MeshRenderer</span><span class="p">(</span>
@@ -366,14 +379,16 @@ If running locally, the data is already available at the correct path.</p>
 </div>
 </div>
 <div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
-</div><div class="inner_cell">
+</div>
+<div class="inner_cell">
 <div class="text_cell_render border-box-sizing rendered_html">
 <h2 id="3.-Render-the-mesh">3. Render the mesh<a class="anchor-link" href="#3.-Render-the-mesh">¶</a></h2>
 </div>
 </div>
 </div>
 <div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
-</div><div class="inner_cell">
+</div>
+<div class="inner_cell">
 <div class="text_cell_render border-box-sizing rendered_html">
 <p>The light is in front of the object so it is bright and the image has specular highlights.</p>
 </div>
@@ -387,7 +402,6 @@ If running locally, the data is already available at the correct path.</p>
 <div class="highlight hl-ipython3"><pre><span></span><span class="n">images</span> <span class="o">=</span> <span class="n">renderer</span><span class="p">(</span><span class="n">mesh</span><span class="p">)</span>
 <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
 <span class="n">plt</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">images</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="o">...</span><span class="p">,</span> <span class="p">:</span><span class="mi">3</span><span class="p">]</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="s2">"off"</span><span class="p">);</span>
 <span class="n">plt</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s2">"off"</span><span class="p">);</span>
 </pre></div>
 </div>
@@ -395,9 +409,10 @@ If running locally, the data is already available at the correct path.</p>
 </div>
 </div>
 <div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
-</div><div class="inner_cell">
+</div>
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-<h2 id="4.-Move-the-light-behind-the-object-and-re-render">4. Move the light behind the object and re-render<a class="anchor-link" href="#4.-Move-the-light-behind-the-object-and-re-render">¶</a></h2><p>We can pass arbirary keyword arguments to the <code>rasterizer</code>/<code>shader</code> via the call to the <code>renderer</code> so the renderer does not need to be reinitialized if any of the settings change/</p>
+<h2 id="4.-Move-the-light-behind-the-object-and-re-render">4. Move the light behind the object and re-render<a class="anchor-link" href="#4.-Move-the-light-behind-the-object-and-re-render">¶</a></h2><p>We can pass arbitrary keyword arguments to the <code>rasterizer</code>/<code>shader</code> via the call to the <code>renderer</code> so the renderer does not need to be reinitialized if any of the settings change/</p>
 <p>In this case, we can simply update the location of the lights and pass them into the call to the renderer.</p>
 <p>The image is now dark as there is only ambient lighting, and there are no specular highlights.</p>
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 <h2 id="5.-Rotate-the-object,-modify-the-material-properties-or-light-properties">5. Rotate the object, modify the material properties or light properties<a class="anchor-link" href="#5.-Rotate-the-object,-modify-the-material-properties-or-light-properties">¶</a></h2><p>We can also change many other settings in the rendering pipeline. Here we:</p>
 <ul>
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 <h2 id="6.-Batched-Rendering">6. Batched Rendering<a class="anchor-link" href="#6.-Batched-Rendering">¶</a></h2><p>One of the core design choices of the PyTorch3D API is to support <strong>batched inputs for all components</strong>. 
 The renderer and associated components can take batched inputs and <strong>render a batch of output images in one forward pass</strong>. We will now use this feature to render the mesh from many different viewpoints.</p>
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 <span class="c1"># so the renderer does not need to be reinitialized if any of the settings change.</span>
 <span class="n">images</span> <span class="o">=</span> <span class="n">renderer</span><span class="p">(</span><span class="n">meshes</span><span class="p">,</span> <span class="n">cameras</span><span class="o">=</span><span class="n">cameras</span><span class="p">,</span> <span class="n">lights</span><span class="o">=</span><span class="n">lights</span><span class="p">)</span>
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 <h2 id="7.-Plotly-visualization">7. Plotly visualization<a class="anchor-link" href="#7.-Plotly-visualization">¶</a></h2><p>If you only want to visualize a mesh, you don't really need to use a differentiable renderer - instead we support plotting of Meshes with plotly. For these Meshes, we use TexturesVertex to define a texture for the rendering.
 <code>plot_meshes</code> creates a Plotly figure with a trace for each Meshes object.</p>
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 <p>For batches, we can also use <code>plot_batch_individually</code> to avoid constructing the scene dictionary ourselves.</p>
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 <p>We can also modify the axis arguments and axis backgrounds in both functions.</p>
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 <h2 id="8.-Conclusion">8. Conclusion<a class="anchor-link" href="#8.-Conclusion">¶</a></h2><p>In this tutorial we learnt how to <strong>load</strong> a textured mesh from an obj file, initialize a PyTorch3D datastructure called <strong>Meshes</strong>, set up an <strong>Renderer</strong> consisting of a <strong>Rasterizer</strong> and a <strong>Shader</strong>, and modify several components of the rendering pipeline. We also learned how to render Meshes in Plotly figures.</p>
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