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Summary: Importing from pytorch3d.visualization is wordy, so shortened the path to the vis module and updated the relevant imports. Reviewed By: nikhilaravi Differential Revision: D24116527 fbshipit-source-id: e0e4da7d48c5afedec07482d7be43362b6822445
28 lines
1.4 KiB
Markdown
28 lines
1.4 KiB
Markdown
---
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hide_title: true
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sidebar_label: Plotly Visualization
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---
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# Overview
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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/vis/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.
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# Examples
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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.
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# Saving plots to images
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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/).
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Install Kaleido
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```
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$ pip install Kaleido
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```
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Export a figure as a .png image. The image will be saved in the current working directory.
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```
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fig = ...
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fig.write_image("image_name.png")
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```
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