Add note explaining plotly visualizations

Summary: Add markdown note explaining why PyTorch3D has plotly visualizations, examples, and how to save these visualizations as an image.

Reviewed By: nikhilaravi

Differential Revision: D23976283

fbshipit-source-id: cbbaffd1f0ebe3466841e42fdb454d85773152cd

Author: Amitav Baruah

docs/notes/assets/plotly_meshes_batch.png

@@ -0,0 +1,27 @@
+---
+hide_title: true
+sidebar_label: Plotly Visualization
+---
+
+# Overview
+
+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 [functions to render meshes and pointclouds in plotly](../../pytorch3d/visualization/plotly_vis.py). 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.
+
+
+# Examples
+
+These rendering functions accept plotly x,y, and z axis arguments as `kwargs`, allowing us to customize the plots. Here are two plots with colored axes, a [Pointclouds plot](assets/plotly_pointclouds.png), a [batched Meshes plot in subplots](assets/plotly_meshes_batch.png), and a [batched Meshes plot with multiple traces](assets/plotly_meshes_trace.png). Refer to the [render textured meshes](../tutorials/render_textured_meshes.ipynb) and [render colored pointclouds](../tutorials/render_colored_points) tutorials for code examples.
+
+# Saving plots to images
+
+If you want to save these plotly plots, you will need to install a separate library such as [Kaleido](https://plotly.com/python/static-image-export/).
+
+Install Kaleido
+```
+$ pip install Kaleido
+```
+Export a figure as a .png image. The image will be saved in the current working directory.
+```
+fig = ...
+fig.write_image("image_name.png")
+```