| --- |
| 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](https://github.com/facebookresearch/pytorch3d/blob/main/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. |
|
|
|
|
| # 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](https://pytorch3d.org/tutorials/render_textured_meshes) and [render colored pointclouds](https://pytorch3d.org/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") |
| ``` |
|
|
