### Install Demos and Examples Dependencies

Source: https://github.com/facebookresearch/pytorch3d/blob/main/INSTALL.md

Installs Python packages required for running PyTorch3D demos, examples, and tutorials using Conda and pip.

```bash
# Demos and examples
conda install jupyter
pip install scikit-image matplotlib imageio plotly opencv-python
```

--------------------------------

### Install PyTorch3D on Windows

Source: https://github.com/facebookresearch/pytorch3d/blob/main/INSTALL.md

Navigate to the cloned directory and use setup.py for installation on Windows. Ensure you are in the correct command prompt environment.

```bash
cd pytorch3d
python3 setup.py install
```

--------------------------------

### Install PyTorch3D from Local Clone

Source: https://github.com/facebookresearch/pytorch3d/blob/main/INSTALL.md

Clone the repository and install using pip. This is the standard method for development or custom builds.

```bash
git clone https://github.com/facebookresearch/pytorch3d.git
cd pytorch3d && pip install -e .
```

--------------------------------

### Install Tutorial Build Dependencies

Source: https://github.com/facebookresearch/pytorch3d/blob/main/website/README.md

Installs Python dependencies required for parsing Jupyter notebooks into HTML for the website tutorials.

```sh
pip install nbformat==4.4.0 nbconvert==5.3.1 ipywidgets==7.5.1 tornado==4.2 bs4
```

--------------------------------

### Start Development Server

Source: https://github.com/facebookresearch/pytorch3d/blob/main/website/README.md

Starts the Docusaurus development server to preview website changes locally. Use either yarn or npm.

```sh
# Start the site
$ yarn start

or
$ ./node_modules/docusaurus/lib/start-server.js
```

--------------------------------

### Install omegaconf

Source: https://github.com/facebookresearch/pytorch3d/blob/main/docs/tutorials/implicitron_config_system.ipynb

Installs the omegaconf library using pip, which is required for managing configurations in Implicitron.

```python
!pip install omegaconf
```

--------------------------------

### Install ffmpeg

Source: https://github.com/facebookresearch/pytorch3d/blob/main/projects/implicitron_trainer/README.md

Install ffmpeg using conda, which is required for the visualization script to produce videos.

```shell
conda install ffmpeg
```

--------------------------------

### Install PyTorch3D

Source: https://github.com/facebookresearch/pytorch3d/blob/main/docs/tutorials/deform_source_mesh_to_target_mesh.ipynb

Installs PyTorch3D and its dependencies. It checks for an existing installation and attempts to install a pre-compiled wheel if available, otherwise falls back to installing from source.

```python
import os
import sys
import torch
import subprocess
need_pytorch3d=False
try:
    import pytorch3d
except ModuleNotFoundError:
    need_pytorch3d=True
if need_pytorch3d:
    pyt_version_str=torch.__version__.split("+")[0].replace(".", "")
    version_str="".join([
        f"py3{sys.version_info.minor}_cu",
        torch.version.cuda.replace(".", רבי),
        f"_pyt{pyt_version_str}"
    ])
    !pip install iopath
    if sys.platform.startswith("linux"):
        print("Trying to install wheel for PyTorch3D")
        !pip install --no-index --no-cache-dir pytorch3d -f https://dl.fbaipublicfiles.com/pytorch3d/packaging/wheels/{version_str}/download.html
        pip_list = !pip freeze
        need_pytorch3d = not any(i.startswith("pytorch3d==") for  i in pip_list)
    if need_pytorch3d:
        print(f"failed to find/install wheel for {version_str}")
if need_pytorch3d:
    print("Installing PyTorch3D from source")
    !pip install ninja
    !pip install 'git+https://github.com/facebookresearch/pytorch3d.git@stable'
```

--------------------------------

### Serve Documentation Locally

Source: https://github.com/facebookresearch/pytorch3d/blob/main/docs/README.md

Starts a simple Python HTTP server to view the generated HTML documentation locally. Navigate to http://0.0.0.0:8000/ in your browser.

```bash
> python -m http.server
```

--------------------------------

### Install PyTorch3D

Source: https://github.com/facebookresearch/pytorch3d/blob/main/docs/tutorials/render_textured_meshes.ipynb

Installs PyTorch3D and its dependencies. This script checks for an existing installation and attempts to install a pre-compiled wheel or falls back to installing from source if necessary. It requires `torch` and `torchvision` to be installed.

```python
import os
import sys
import torch
import subprocess
need_pytorch3d=False
try:
    import pytorch3d
except ModuleNotFoundError:
    need_pytorch3d=True
if need_pytorch3d:
    pyt_version_str=torch.__version__.split("+")[0].replace(".", "")
    version_str="".join([
        f"py3{sys.version_info.minor}_cu",
        torch.version.cuda.replace("."," "),
        f"_pyt{pyt_version_str}"
    ])
    !pip install iopath
    if sys.platform.startswith("linux"):
        print("Trying to install wheel for PyTorch3D")
        !pip install --no-index --no-cache-dir pytorch3d -f https://dl.fbaipublicfiles.com/pytorch3d/packaging/wheels/{version_str}/download.html
        pip_list = !pip freeze
        need_pytorch3d = not any(i.startswith("pytorch3d==") for  i in pip_list)
    if need_pytorch3d:
        print(f"failed to find/install wheel for {version_str}")
if need_pytorch3d:
    print("Installing PyTorch3D from source")
    !pip install ninja
    !pip install 'git+https://github.com/facebookresearch/pytorch3d.git@stable'
```

--------------------------------

### Start Visdom Server

Source: https://github.com/facebookresearch/pytorch3d/blob/main/projects/nerf/README.md

Launches the Visdom server, which is used for outputting visualizations during NeRF training.

```bash
python -m visdom.server
```

--------------------------------

### Install PyTorch3D

Source: https://github.com/facebookresearch/pytorch3d/blob/main/docs/tutorials/implicitron_volumes.ipynb

Installs PyTorch3D and its dependencies, including iopath and omegaconf. It attempts to install a pre-built wheel if available, otherwise falls back to installing from source.

```python
import os
import sys
import torch
import subprocess
need_pytorch3d=False
try:
    import pytorch3d
except ModuleNotFoundError:
    need_pytorch3d=True
if need_pytorch3d:
    pyt_version_str=torch.__version__.split("+")[0].replace(".", "")
    version_str="".join([
        f"py3{sys.version_info.minor}_cu",
        torch.version.cuda.replace("."," "),
        f"_pyt{pyt_version_str}"
    ])
    !pip install iopath
    if sys.platform.startswith("linux"):
        print("Trying to install wheel for PyTorch3D")
        !pip install --no-index --no-cache-dir pytorch3d -f https://dl.fbaipublicfiles.com/pytorch3d/packaging/wheels/{version_str}/download.html
        pip_list = !pip freeze
        need_pytorch3d = not any(i.startswith("pytorch3d==") for  i in pip_list)
    if need_pytorch3d:
        print(f"failed to find/install wheel for {version_str}")
if need_pytorch3d:
    print("Installing PyTorch3D from source")
    !pip install ninja
    !pip install 'git+https://github.com/facebookresearch/pytorch3d.git@stable'
```

```python
!pip install omegaconf visdom
```

--------------------------------

### Install PyTorch3D from GitHub (Stable Branch)

Source: https://github.com/facebookresearch/pytorch3d/blob/main/INSTALL.md

Installs PyTorch3D from the 'stable' branch of its GitHub repository using pip. This installs the latest released version.

```bash
pip install "git+https://github.com/facebookresearch/pytorch3d.git@stable"
```

--------------------------------

### Install Documentation Dependencies

Source: https://github.com/facebookresearch/pytorch3d/blob/main/docs/README.md

Installs essential Python packages for Sphinx documentation generation and Markdown table support.

```bash
pip install -U recommonmark sphinx sphinx_rtd_theme sphinx_markdown_tables
```

--------------------------------

### Install PyTorch3D

Source: https://github.com/facebookresearch/pytorch3d/blob/main/docs/tutorials/camera_position_optimization_with_differentiable_rendering.ipynb

Installs PyTorch3D from wheels or source, depending on availability and system configuration. Ensure PyTorch and torchvision are installed.

```python
import os
import sys
import torch
import subprocess
need_pytorch3d=False
try:
    import pytorch3d
except ModuleNotFoundError:
    need_pytorch3d=True
if need_pytorch3d:
    pyt_version_str=torch.__version__.split("+")[0].replace(".", "")
    version_str="".join([
        f"py3{sys.version_info.minor}_cu",
        torch.version.cuda.replace(".", ""),
        f"_pyt{pyt_version_str}"
    ])
    !pip install iopath
    if sys.platform.startswith("linux"):
        print("Trying to install wheel for PyTorch3D")
        !pip install --no-index --no-cache-dir pytorch3d -f https://dl.fbaipublicfiles.com/pytorch3d/packaging/wheels/{version_str}/download.html
        pip_list = !pip freeze
        need_pytorch3d = not any(i.startswith("pytorch3d==") for  i in pip_list)
    if need_pytorch3d:
        print(f"failed to find/install wheel for {version_str}")
if need_pytorch3d:
    print("Installing PyTorch3D from source")
    !pip install ninja
    !pip install 'git+https://github.com/facebookresearch/pytorch3d.git@stable'
```

--------------------------------

### Install Dependencies with Pip

Source: https://github.com/facebookresearch/pytorch3d/blob/main/projects/nerf/README.md

Installs necessary Python packages for PyTorch3D NeRF implementation using pip.

```bash
pip install visdom
pip install hydra-core --upgrade
pip install Pillow
pip install requests
```

--------------------------------

### Install Chumpy

Source: https://github.com/facebookresearch/pytorch3d/blob/main/docs/tutorials/render_densepose.ipynb

Installs the chumpy library, which is required for loading SMPL model pickle files.

```python
# We also install chumpy as it is needed to load the SMPL model pickle file.
!pip install chumpy
```

--------------------------------

### Setup PyTorch3D Renderer

Source: https://github.com/facebookresearch/pytorch3d/blob/main/docs/tutorials/render_densepose.ipynb

Configures a MeshRenderer with a camera, rasterization settings, and a Phong shader. This setup is for visualizing the textured mesh.

```python
# Initialize a camera.
# World coordinates +Y up, +X left and +Z in.
R, T = look_at_view_transform(2.7, 0, 0) 
cameras = FoVPerspectiveCameras(device=device, R=R, T=T)

# Define the settings for rasterization and shading. Here we set the output image to be of size
# 512x512. As we are rendering images for visualization purposes only we will set faces_per_pixel=1
# and blur_radius=0.0. 
raster_settings = RasterizationSettings(
    image_size=512, 
    blur_radius=0.0, 
    faces_per_pixel=1,
)

# Place a point light in front of the person. 
lights = PointLights(device=device, location=[[0.0, 0.0, 2.0]])

# Create a Phong renderer by composing a rasterizer and a shader. The textured Phong shader will 
# interpolate the texture uv coordinates for each vertex, sample from a texture image and 
# apply the Phong lighting model
renderer = MeshRenderer(
    rasterizer=MeshRasterizer(
        cameras=cameras, 
        raster_settings=raster_settings
    ),
    shader=SoftPhongShader(
        device=device, 
        cameras=cameras,
        lights=lights
    )
)
```

--------------------------------

### Install Kaleido for Plotly Image Export

Source: https://github.com/facebookresearch/pytorch3d/blob/main/docs/notes/visualization.md

Install the Kaleido library to enable saving Plotly figures as static images.

```bash
$ pip install Kaleido
```

--------------------------------

### Initialize Renderer Components

Source: https://github.com/facebookresearch/pytorch3d/blob/main/docs/tutorials/camera_position_optimization_with_differentiable_rendering.ipynb

Initializes a perspective camera, blend parameters for rendering, and rasterization settings. This setup is for silhouette rendering.

```python
# Initialize a perspective camera.
cameras = FoVPerspectiveCameras(device=device)

# To blend the 100 faces we set a few parameters which control the opacity and the sharpness of 
# edges. Refer to blending.py for more details. 
blend_params = BlendParams(sigma=1e-4, gamma=1e-4)

# Define the settings for rasterization and shading. Here we set the output image to be of size
# 256x256. To form the blended image we use 100 faces for each pixel. We also set bin_size and max_faces_per_bin to None which ensure that 

```

--------------------------------

### Install Website Dependencies

Source: https://github.com/facebookresearch/pytorch3d/blob/main/website/README.md

Installs the necessary Node.js dependencies for the Docusaurus website. Use either yarn or npm.

```sh
# Install dependencies
$ yarn

or

$ npm install docusaurus-init
```

--------------------------------

### Import PyTorch3D Modules and Setup Device

Source: https://github.com/facebookresearch/pytorch3d/blob/main/docs/tutorials/deform_source_mesh_to_target_mesh.ipynb

Imports necessary PyTorch3D modules and sets up the computation device (CUDA if available, otherwise CPU). Includes plotting configurations.

```python
import os
import torch
from pytorch3d.io import load_obj, save_obj
from pytorch3d.structures import Meshes
from pytorch3d.utils import ico_sphere
from pytorch3d.ops import sample_points_from_meshes
from pytorch3d.loss import (
    chamfer_distance, 
    mesh_edge_loss, 
    mesh_laplacian_smoothing, 
    mesh_normal_consistency,
)
import numpy as np
from tqdm.notebook import tqdm
%matplotlib notebook 
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import matplotlib as mpl


mpl.rcParams['savefig.dpi'] = 80
mpl.rcParams['figure.dpi'] = 80

# Set the device
if torch.cuda.is_available():
    device = torch.device("cuda:0")
else:
    device = torch.device("cpu")
    print("WARNING: CPU only, this will be slow!")
```

--------------------------------

### Instantiating PerspectiveCameras in NDC and Screen Space

Source: https://github.com/facebookresearch/pytorch3d/blob/main/docs/notes/cameras.md

Example demonstrating how to create equivalent PerspectiveCameras instances in both NDC and screen coordinate spaces. For screen space, image size and `in_ndc=False` must be provided.

```python
# NDC space camera
fcl_ndc = (1.2,)
prp_ndc = ((0.2, 0.5),)
cameras_ndc = PerspectiveCameras(focal_length=fcl_ndc, principal_point=prp_ndc)

# Screen space camera
image_size = ((128, 256),)    # (h, w)
fcl_screen = (76.8,)          # fcl_ndc * min(image_size) / 2
prp_screen = ((115.2, 32), )  # w / 2 - px_ndc * min(image_size) / 2, h / 2 - py_ndc * min(image_size) / 2
cameras_screen = PerspectiveCameras(focal_length=fcl_screen, principal_point=prp_screen, in_ndc=False, image_size=image_size)
```

--------------------------------

### Install PyTorch3D from GitHub (Main Branch)

Source: https://github.com/facebookresearch/pytorch3d/blob/main/INSTALL.md

Installs PyTorch3D directly from the main branch of its GitHub repository using pip. This provides the latest development version.

```bash
pip install "git+https://github.com/facebookresearch/pytorch3d.git"
```

--------------------------------

### PerspectiveCameras Setup and Projection

Source: https://context7.com/facebookresearch/pytorch3d/llms.txt

Configures a perspective camera using focal length and principal point, then projects 3D world points to screen coordinates. Ensure correct device placement.

```python
import torch
from pytorch3d.renderer import PerspectiveCameras

# Batch of 2 cameras
R = torch.eye(3).unsqueeze(0).expand(2, -1, -1)   # (2, 3, 3)
T = torch.zeros(2, 3)                               # (2, 3)

cameras = PerspectiveCameras(
    focal_length=((500., 500.),),   # (fx, fy) in pixels
    principal_point=((256., 256.),),
    image_size=((512, 512),),
    in_ndc=False,
    R=R[:1], T=T[:1],
    device="cpu",
)

# Project 3D world points to 2D screen
pts_world = torch.randn(1, 10, 3)
pts_screen = cameras.transform_points_screen(pts_world, image_size=(512, 512))
# pts_screen: (1, 10, 3) — xy pixel coords + depth

# Get camera center in world coordinates
center = cameras.get_camera_center()  # (1, 3)
```

--------------------------------

### Install PyTorch3D with CUDA from Anaconda Cloud

Source: https://github.com/facebookresearch/pytorch3d/blob/main/INSTALL.md

Installs the prebuilt PyTorch3D binary with CUDA support from the Anaconda Cloud channel. This is for Linux only.

```bash
conda install pytorch3d -c pytorch3d
```

--------------------------------

### Install PyTorch3D Wheels Dynamically

Source: https://github.com/facebookresearch/pytorch3d/blob/main/INSTALL.md

Installs PyTorch3D using prebuilt wheels by dynamically determining the correct wheel URL based on the current Python, CUDA, and PyTorch versions. Useful within IPython, Google Colab, or Jupyter notebooks.

```python
import sys
import torch
pyt_version_str=torch.__version__.split("+")[0].replace(".", "")
version_str="".join([
    f"py3{sys.version_info.minor}_cu",
    torch.version.cuda.replace(".", ""),
    f"_pyt{pyt_version_str}"
])
!pip install iopath
!pip install --no-index --no-cache-dir pytorch3d -f https://dl.fbaipublicfiles.com/pytorch3d/packaging/wheels/{version_str}/download.html
```

--------------------------------

### Install PyTorch3D on macOS

Source: https://github.com/facebookresearch/pytorch3d/blob/main/INSTALL.md

Use specific environment variables for the build process on macOS. Ensure CC and CXX are set to clang.

```bash
MACOSX_DEPLOYMENT_TARGET=10.14 CC=clang CXX=clang++ pip install -e .
```

--------------------------------

### Initialize Source Mesh

Source: https://github.com/facebookresearch/pytorch3d/blob/main/docs/tutorials/fit_textured_mesh.ipynb

Loads the initial sphere mesh which will be deformed. This is the starting point for the optimization process.

```python
# We initialize the source shape to be a sphere of radius 1.  
src_mesh = ico_sphere(4, device)
```

--------------------------------

### Setup Optimizer

Source: https://github.com/facebookresearch/pytorch3d/blob/main/docs/tutorials/deform_source_mesh_to_target_mesh.ipynb

Configures the SGD optimizer to update the `deform_verts` tensor.

```python
# The optimizer
optimizer = torch.optim.SGD([deform_verts], lr=1.0, momentum=0.9)
```

--------------------------------

### Install PyTorch3D from GitHub on macOS

Source: https://github.com/facebookresearch/pytorch3d/blob/main/INSTALL.md

Installs PyTorch3D from GitHub on macOS, setting necessary environment variables for the compiler and deployment target. This ensures compatibility with macOS build environments.

```bash
MACOSX_DEPLOYMENT_TARGET=10.14 CC=clang CXX=clang++ pip install "git+https://github.com/facebookresearch/pytorch3d.git"
```

--------------------------------

### Install PyTorch3D Wheels for Linux (Specific Version)

Source: https://github.com/facebookresearch/pytorch3d/blob/main/INSTALL.md

Installs PyTorch3D using prebuilt wheels for Linux, specifying Python, CUDA, and PyTorch versions. This is useful for environments like Google Colab.

```bash
pip install --no-index --no-cache-dir pytorch3d -f https://dl.fbaipublicfiles.com/pytorch3d/packaging/wheels/py38_cu113_pyt1110/download.html
```

--------------------------------

### Fetch Mesh and Texture Files

Source: https://github.com/facebookresearch/pytorch3d/blob/main/docs/tutorials/render_textured_meshes.ipynb

Downloads the `cow.obj`, `cow.mtl`, and `cow_texture.png` files required for the tutorial. These files are saved into the `data/cow_mesh` directory. This step is for Google Colab users; local installations typically have these files pre-existing.

```shell
!mkdir -p data/cow_mesh
!wget -P data/cow_mesh https://dl.fbaipublicfiles.com/pytorch3d/data/cow_mesh/cow.obj
!wget -P data/cow_mesh https://dl.fbaipublicfiles.com/pytorch3d/data/cow_mesh/cow.mtl
!wget -P data/cow_mesh https://dl.fbaipublicfiles.com/pytorch3d/data/cow_mesh/cow_texture.png
```

--------------------------------

### Initialize Renderer Components

Source: https://github.com/facebookresearch/pytorch3d/blob/main/docs/tutorials/render_textured_meshes.ipynb

Set up a perspective camera, point lights, and rasterization settings for rendering. Ensure the camera is oriented correctly towards the object. Use default values for unspecified components.

```python
R, T = look_at_view_transform(2.7, 0, 180)
cameras = FoVPerspectiveCameras(device=device, R=R, T=T)

raster_settings = RasterizationSettings(
    image_size=512, 
    blur_radius=0.0, 
    faces_per_pixel=1,
)

lights = PointLights(device=device, location=[[0.0, 0.0, -3.0]])

renderer = MeshRenderer(
    rasterizer=MeshRasterizer(
        cameras=cameras, 
        raster_settings=raster_settings
    ),
    shader=SoftPhongShader(
        device=device, 
        cameras=cameras,
        lights=lights
    )
)
```

--------------------------------

### Initialize ShapeNetCore Dataset

Source: https://github.com/facebookresearch/pytorch3d/blob/main/docs/tutorials/dataloaders_ShapeNetCore_R2N2.ipynb

Set up the device and initialize the ShapeNetCore dataset. Ensure SHAPENET_PATH is correctly set to your dataset location.

```python
if torch.cuda.is_available():
    device = torch.device("cuda:0")
    torch.cuda.set_device(device)
else:
    device = torch.device("cpu")
    
SHAPENET_PATH = ""
shapenet_dataset = ShapeNetCore(SHAPENET_PATH)
```

--------------------------------

### Initialize Cameras and Lights

Source: https://github.com/facebookresearch/pytorch3d/blob/main/docs/tutorials/render_textured_meshes.ipynb

Set up camera transformations using look_at_view_transform and define light locations for rendering.

```python
R, T = look_at_view_transform(dist=2.7, elev=elev, azim=azim)
cameras = FoVPerspectiveCameras(device=device, R=R, T=T)

# Move the light back in front of the cow which is facing the -z direction.
lights.location = torch.tensor([[0.0, 0.0, -3.0]], device=device)
```

--------------------------------

### Initialize Cameras and Lights

Source: https://github.com/facebookresearch/pytorch3d/blob/main/docs/tutorials/fit_textured_mesh.ipynb

Sets up multiple camera viewpoints and a point light for rendering. Use `look_at_view_transform` to define camera extrinsics.

```python
num_views = 20

elev = torch.linspace(0, 360, num_views)
azim = torch.linspace(-180, 180, num_views)

lights = PointLights(device=device, location=[[0.0, 0.0, -3.0]])

R, T = look_at_view_transform(dist=2.7, elev=elev, azim=azim)
cameras = FoVPerspectiveCameras(device=device, R=R, T=T)

camera = FoVPerspectiveCameras(device=device, R=R[None, 1, ...], 
                                  T=T[None, 1, ...])
```

--------------------------------

### Install Implicitron Dependencies

Source: https://github.com/facebookresearch/pytorch3d/blob/main/projects/implicitron_trainer/README.md

Install necessary dependencies for Implicitron, including Hydra, Visdom, LPIPS, Matplotlib, and Accelerate. This is required for Option 1 and Option 3 installations.

```shell
pip install "hydra-core>=1.1" visdom lpips matplotlib accelerate
```

--------------------------------

### Install ioPath with Conda

Source: https://github.com/facebookresearch/pytorch3d/blob/main/INSTALL.md

Installs the ioPath library, a dependency for PyTorch3D, using Conda.

```bash
conda install -c iopath iopath
```

--------------------------------

### Initialize Loss Dictionary and Parameters

Source: https://github.com/facebookresearch/pytorch3d/blob/main/docs/tutorials/fit_textured_mesh.ipynb

Sets up the loss components, weights, and storage for their values, along with parameters for mesh deformation and vertex colors. Requires PyTorch and device configuration.

```python
# Number of views to optimize over in each SGD iteration
num_views_per_iteration = 2
# Number of optimization steps
Niter = 2000
# Plot period for the losses
plot_period = 250

%matplotlib inline

# Optimize using rendered RGB image loss, rendered silhouette image loss, mesh 
# edge loss, mesh normal consistency, and mesh laplacian smoothing
losses = {"rgb": {"weight": 1.0, "values": []},
          "silhouette": {"weight": 1.0, "values": []},
          "edge": {"weight": 1.0, "values": []},
          "normal": {"weight": 0.01, "values": []},
          "laplacian": {"weight": 1.0, "values": []},
         }

# We will learn to deform the source mesh by offsetting its vertices
# The shape of the deform parameters is equal to the total number of vertices in 
# src_mesh
verts_shape = src_mesh.verts_packed().shape
deform_verts = torch.full(verts_shape, 0.0, device=device, requires_grad=True)

# We will also learn per vertex colors for our sphere mesh that define texture 
# of the mesh
sphere_verts_rgb = torch.full([1, verts_shape[0], 3], 0.5, device=device, requires_grad=True)
```

--------------------------------

### Install PyTorch3D Wheels in Jupyter/Colab

Source: https://github.com/facebookresearch/pytorch3d/blob/main/packaging/linux_wheels/README.md

Installs PyTorch3D wheels in a Jupyter or Colab notebook. It first determines the correct version string based on PyTorch and CUDA versions, then installs iopath and the PyTorch3D wheel from a specified URL.

```python
import sys
import torch
pyt_version_str=torch.__version__.split("+")[0].replace(".", "")
version_str="".join([
    f"py3{sys.version_info.minor}_cu",
    torch.version.cuda.replace(".","",2),
    f"_pyt{pyt_version_str}"
])
!pip install iopath
!pip install --no-index --no-cache-dir pytorch3d -f https://dl.fbaipublicfiles.com/pytorch3d/packaging/wheels/{version_str}/download.html
```

--------------------------------

### Install Tests/Linting Dependencies

Source: https://github.com/facebookresearch/pytorch3d/blob/main/INSTALL.md

Installs Python packages for linting and testing PyTorch3D using Conda and pip.

```bash
# Tests/Linting
conda install -c fvcore -c conda-forge fvcore
pip install black usort flake8 flake8-bugbear flake8-comprehensions
```

--------------------------------

### Instantiating and Using a Configurable Module

Source: https://github.com/facebookresearch/pytorch3d/blob/main/docs/tutorials/implicitron_config_system.ipynb

Create an instance of `MyLinear`, pass a dummy input, and print the shape of the output. This demonstrates basic usage of the configurable PyTorch module.

```python
my_linear = MyLinear()
input = torch.zeros(2)
output = my_linear(input)
print("output shape:", output.shape)
```

--------------------------------

### Install PyTorch3D Main Branch from Source in Colab

Source: https://github.com/facebookresearch/pytorch3d/blob/main/docs/tutorials/README.md

This command installs the latest PyTorch3D from the main branch via pip, suitable for Colab environments. It's an alternative to the stable installation and may be necessary if you are working with the most recent development versions.

```bash
!pip install ‘git+https://github.com/facebookresearch/pytorch3d.git’
```

--------------------------------

### Initialize PyTorch3D Renderer

Source: https://github.com/facebookresearch/pytorch3d/blob/main/docs/notes/renderer_getting_started.md

Sets up a basic PyTorch3D renderer using a perspective camera, rasterization settings, and a Phong shader. Ensure the 'device' variable is defined before use.

```python
from pytorch3d.renderer import (

 FoVPerspectiveCameras, look_at_view_transform,
 RasterizationSettings, BlendParams,
 MeshRenderer, MeshRasterizer, HardPhongShader

)

# Initialize an OpenGL perspective camera.
R, T = look_at_view_transform(2.7, 10, 20)
cameras = FoVPerspectiveCameras(device=device, R=R, T=T)

# Define the settings for rasterization and shading. Here we set the output image to be of size
# 512x512. As we are rendering images for visualization purposes only we will set faces_per_pixel=1
# and blur_radius=0.0. Refer to rasterize_meshes.py for explanations of these parameters.
raster_settings = RasterizationSettings(

 image_size=512,
 blur_radius=0.0,
 faces_per_pixel=1,

)

# Create a Phong renderer by composing a rasterizer and a shader. Here we can use a predefined
# PhongShader, passing in the device on which to initialize the default parameters
renderer = MeshRenderer(

 rasterizer=MeshRasterizer(cameras=cameras, raster_settings=raster_settings),
 shader=HardPhongShader(device=device, cameras=cameras)

# )
```

--------------------------------

### Load and prepare point cloud data

Source: https://github.com/facebookresearch/pytorch3d/blob/main/docs/tutorials/render_colored_points.ipynb

Sets up the device (GPU or CPU), defines data paths, loads point cloud vertices and RGB colors from a .npz file, and creates a PyTorch3D Pointclouds object.

```python
# Setup
if torch.cuda.is_available():
    device = torch.device("cuda:0")
    torch.cuda.set_device(device)
else:
    device = torch.device("cpu")

# Set paths
DATA_DIR = "./data"
obj_filename = os.path.join(DATA_DIR, "PittsburghBridge/pointcloud.npz")

# Load point cloud
pointcloud = np.load(obj_filename)
verts = torch.Tensor(pointcloud['verts']).to(device)
        
rgb = torch.Tensor(pointcloud['rgb']).to(device)

point_cloud = Pointclouds(points=[verts], features=[rgb])
```

--------------------------------

### Import Modules and Set Up Device

Source: https://github.com/facebookresearch/pytorch3d/blob/main/docs/tutorials/bundle_adjustment.ipynb

Imports necessary PyTorch3D modules for camera transformations and rendering, sets up the device (GPU or CPU), and configures manual seeding for reproducibility.

```python
# imports
import torch
from pytorch3d.transforms.so3 import (
    so3_exp_map,
    so3_relative_angle,
)
from pytorch3d.renderer.cameras import (
    SfMPerspectiveCameras,
)

# add path for demo utils
import sys
import os
sys.path.append(os.path.abspath(''))

# set for reproducibility
torch.manual_seed(42)
if torch.cuda.is_available():
    device = torch.device("cuda:0")
else:
    device = torch.device("cpu")
    print("WARNING: CPU only, this will be slow!")
```

--------------------------------

### Initialize Model and Optimizer

Source: https://github.com/facebookresearch/pytorch3d/blob/main/docs/tutorials/camera_position_optimization_with_differentiable_rendering.ipynb

Initializes the optimization model and sets up an Adam optimizer to train the camera position parameter. Also prepares an image writer for saving optimization progress.

```python
# We will save images periodically and compose them into a GIF.
filename_output = "./teapot_optimization_demo.gif"
writer = imageio.get_writer(filename_output, mode='I', duration=0.3)

# Initialize a model using the renderer, mesh and reference image
model = Model(meshes=teapot_mesh, renderer=silhouette_renderer, image_ref=image_ref).to(device)

# Create an optimizer. Here we are using Adam and we pass in the parameters of the model
optimizer = torch.optim.Adam(model.parameters(), lr=0.05)
```

--------------------------------

### Initialize Volume Model and Optimizer

Source: https://github.com/facebookresearch/pytorch3d/blob/main/docs/tutorials/fit_textured_volume.ipynb

Sets up the volumetric model and the Adam optimizer. The learning rate is initialized to 0.1 and can be decreased during training.

```python
volume_size = 128
volume_model = VolumeModel(
    renderer,
    volume_size=[volume_size] * 3, 
    voxel_size = volume_extent_world / volume_size,
).to(device)

# Instantiate the Adam optimizer. We set its master learning rate to 0.1.
lr = 0.1
optimizer = torch.optim.Adam(volume_model.parameters(), lr=lr)
```

--------------------------------

### Build Website and Parse Tutorials

Source: https://github.com/facebookresearch/pytorch3d/blob/main/website/README.md

Executes a script to build the Docusaurus website and parse Jupyter notebooks into HTML, JS, and other assets.

```sh
bash scripts/build_website.sh
```

--------------------------------

### Install CUB with Conda for older CUDA

Source: https://github.com/facebookresearch/pytorch3d/blob/main/INSTALL.md

Installs the CUB library using Conda, which is a build-time dependency for CUDA versions older than 11.7.

```bash
conda install -c bottler nvidiacub
```

--------------------------------

### Download utility scripts

Source: https://github.com/facebookresearch/pytorch3d/blob/main/docs/tutorials/fit_textured_volume.ipynb

Downloads helper scripts for plotting image grids and generating cow renders, which are used later in the tutorial. These are necessary for data generation and visualization.

```python
!wget https://raw.githubusercontent.com/facebookresearch/pytorch3d/main/docs/tutorials/utils/plot_image_grid.py
!wget https://raw.githubusercontent.com/facebookresearch/pytorch3d/main/docs/tutorials/utils/generate_cow_renders.py
from plot_image_grid import image_grid
from generate_cow_renders import generate_cow_renders
```

--------------------------------

### Rebuild PyTorch3D After Installation

Source: https://github.com/facebookresearch/pytorch3d/blob/main/INSTALL.md

To rebuild the project after installing from a local clone, remove previous build artifacts and reinstall. This is often necessary after updating PyTorch.

```bash
rm -rf build/ **/*.so
pip install -e .
```

--------------------------------

### Initialize Model and Optimizer

Source: https://github.com/facebookresearch/pytorch3d/blob/main/docs/tutorials/fit_simple_neural_radiance_field.ipynb

Move relevant variables to the correct device and instantiate the NeuralRadianceField model and Adam optimizer. Set the learning rate for the optimizer.

```python
renderer_grid = renderer_grid.to(device)
renderer_mc = renderer_mc.to(device)
target_cameras = target_cameras.to(device)
target_images = target_images.to(device)
target_silhouettes = target_silhouettes.to(device)

torch.manual_seed(1)

neural_radiance_field = NeuralRadianceField().to(device)

lr = 1e-3
optimizer = torch.optim.Adam(neural_radiance_field.parameters(), lr=lr)
```

--------------------------------

### Install Nightly PyTorch3D Build from Anaconda Cloud

Source: https://github.com/facebookresearch/pytorch3d/blob/main/INSTALL.md

Installs a nightly (alpha) build of PyTorch3D with CUDA support from the Anaconda Cloud channel. This is for Linux only.

```bash
conda install pytorch3d -c pytorch3d-nightly
```

--------------------------------

### Create and Access DictConfig

Source: https://github.com/facebookresearch/pytorch3d/blob/main/docs/tutorials/implicitron_config_system.ipynb

Demonstrates creating a DictConfig from a dictionary and accessing its elements using both dot notation and dictionary-style keys.

```python
dc = DictConfig({"a": 2, "b": True, "c": None, "d": "hello"})
assert dc.a == dc["a"] == 2
```

--------------------------------

### Install Yarn Package Manager

Source: https://github.com/facebookresearch/pytorch3d/blob/main/website/README.md

Installs the Yarn package manager using Homebrew or a direct curl script. Yarn is used for managing website dependencies.

```sh
brew install yarn

# or

curl -o- -L https://yarnpkg.com/install.sh | bash
```

--------------------------------

### Import utility functions

Source: https://github.com/facebookresearch/pytorch3d/blob/main/docs/tutorials/fit_simple_neural_radiance_field.ipynb

Imports the `image_grid` and `generate_cow_renders` functions after they have been downloaded. This makes them available for use in the rest of the notebook.

```python
from plot_image_grid import image_grid
from generate_cow_renders import generate_cow_renders
```

--------------------------------

### Build HTML Documentation

Source: https://github.com/facebookresearch/pytorch3d/blob/main/docs/README.md

Command to build the HTML version of the documentation from the docs directory using the Sphinx make utility.

```bash
> make html
```

--------------------------------

### Install PyTorch, Torchvision, and CUDA with Conda

Source: https://github.com/facebookresearch/pytorch3d/blob/main/INSTALL.md

Installs specific versions of PyTorch, torchvision, and CUDA using Conda channels. This is a common step for setting up the core PyTorch environment.

```bash
conda install pytorch=1.13.0 torchvision pytorch-cuda=11.6 -c pytorch -c nvidia
```

--------------------------------

### Visualize Initial and Reference Camera Positions

Source: https://github.com/facebookresearch/pytorch3d/blob/main/docs/tutorials/camera_position_optimization_with_differentiable_rendering.ipynb

Displays the initial rendered image from the model and the reference silhouette for comparison. Requires matplotlib for plotting.

```python
plt.figure(figsize=(10, 10))

_, image_init = model()
plt.subplot(1, 2, 1)
plt.imshow(image_init.detach().squeeze().cpu().numpy()[..., 3])
plt.grid(False)
plt.title("Starting position")

plt.subplot(1, 2, 2)
plt.imshow(model.image_ref.cpu().numpy().squeeze())
plt.grid(False)
plt.title("Reference silhouette");

```

--------------------------------

### Render Mesh with Initial Settings

Source: https://github.com/facebookresearch/pytorch3d/blob/main/docs/tutorials/render_textured_meshes.ipynb

Render the mesh using the initialized renderer. This will produce an image with lighting and highlights based on the initial camera and light positions.

```python
images = renderer(mesh)
plt.figure(figsize=(10, 10))
plt.imshow(images[0, ..., :3].cpu().numpy())
plt.axis("off")
```

--------------------------------

### Initialize Dataclass from Structured DictConfig

Source: https://github.com/facebookresearch/pytorch3d/blob/main/docs/tutorials/implicitron_config_system.ipynb

Demonstrates how to populate a structured DictConfig with values and then use it to initialize an instance of the original dataclass.

```python
structured.a = 21
my_dataclass_instance2 = MyDataclass(**structured)
print(my_dataclass_instance2)
```

--------------------------------

### Install PyTorch3D from Source in Colab

Source: https://github.com/facebookresearch/pytorch3d/blob/main/docs/tutorials/README.md

Use this command to install PyTorch3D from source within a Google Colab notebook, especially when GPU support or specific dependencies like CUB are needed. This method ensures compatibility with the main branch of PyTorch3D.

```bash
import os
!curl -LO https://github.com/NVIDIA/cub/archive/1.10.0.tar.gz
!tar xzf 1.10.0.tar.gz
os.environ["CUB_HOME"] = os.getcwd() + "/cub-1.10.0"
!pip install 'git+https://github.com/facebookresearch/pytorch3d.git@stable'
```

--------------------------------

### Set up Implicit/NeRF-style Rendering Components

Source: https://context7.com/facebookresearch/pytorch3d/llms.txt

Configure `ImplicitRenderer`, `NDCMultinomialRaysampler`, and `EmissionAbsorptionRaymarcher` for NeRF-style rendering. Define camera, ray sampling parameters (number of points, depth range), and image dimensions.

```python
import torch
from pytorch3d.renderer import (
    FoVPerspectiveCameras,
    NDCMultinomialRaysampler,
    EmissionAbsorptionRaymarcher,
    ImplicitRenderer,
)

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
cameras = FoVPerspectiveCameras(device=device)

# Raysampler: creates a grid of rays from the image plane
raysampler = NDCMultinomialRaysampler(
    image_height=64,
    image_width=64,
    n_pts_per_ray=128,
    min_depth=0.1,
    max_depth=4.0,
)
```

--------------------------------

### Initialize Optimization Parameters

Source: https://github.com/facebookresearch/pytorch3d/blob/main/docs/tutorials/bundle_adjustment.ipynb

Initializes the absolute camera log-rotations and translations for optimization. Sets the first camera to identity and creates a mask for optimization.

```python
# initialize the absolute log-rotations/translations with random entries
log_R_absolute_init = torch.randn(N, 3, dtype=torch.float32, device=device)
T_absolute_init = torch.randn(N, 3, dtype=torch.float32, device=device)

# furthermore, we know that the first camera is a trivial one 
#    (see the description above)
log_R_absolute_init[0, :] = 0.
T_absolute_init[0, :] = 0.

# instantiate a copy of the initialization of log_R / T
log_R_absolute = log_R_absolute_init.clone().detach()
log_R_absolute.requires_grad = True
T_absolute = T_absolute_init.clone().detach()
T_absolute.requires_grad = True

# the mask the specifies which cameras are going to be optimized
#     (since we know the first camera is already correct, 
#      we only optimize over the 2nd-to-last cameras)
camera_mask = torch.ones(N, 1, dtype=torch.float32, device=device)
camera_mask[0] = 0.

# init the optimizer
optimizer = torch.optim.SGD([log_R_absolute, T_absolute], lr=.1, momentum=0.9)
```

--------------------------------

### Markdown Document Structure

Source: https://github.com/facebookresearch/pytorch3d/blob/main/website/README.md

Example front matter for a Docusaurus markdown document. Includes 'id' for linking and 'title' for display.

```markdown
---
id: page-needs-edit
title: This Doc Needs To Be Edited
---

Edit me...

```

--------------------------------

### Prepare for Batched Rendering

Source: https://github.com/facebookresearch/pytorch3d/blob/main/docs/tutorials/render_textured_meshes.ipynb

Set up for batched rendering by extending a single mesh to create a batch of identical meshes. This prepares the input for rendering the same object from multiple viewpoints in a single forward pass.

```python
batch_size = 20
meshes = mesh.extend(batch_size)

elev = torch.linspace(0, 180, batch_size)
azim = torch.linspace(-180, 180, batch_size)
```

--------------------------------

### Blog Post Markdown Structure

Source: https://github.com/facebookresearch/pytorch3d/blob/main/website/README.md

Example front matter for a Docusaurus blog post. Includes 'id', 'title', and author information.

```markdown
---
id: post-needs-edit
title: This Blog Post Needs To Be Edited
---

Edit me...

```

--------------------------------

### Getting Default Arguments for a Configurable Class

Source: https://github.com/facebookresearch/pytorch3d/blob/main/docs/tutorials/implicitron_config_system.ipynb

Use `get_default_args` to retrieve the default configuration arguments for a configurable class, which can then be printed as YAML.

```python
Out_dc = get_default_args(Out)
print(OmegaConf.to_yaml(Out_dc))
```

--------------------------------

### pytorch3d.common.get_device

Source: https://github.com/facebookresearch/pytorch3d/blob/main/docs/modules/common.md

Gets the device of a PyTorch tensor, falling back to a default CPU device if necessary. Allows overriding with an explicit device.

```APIDOC
## pytorch3d.common.get_device

### Description
Gets the device of the specified variable x if it is a tensor, or falls back to a default CPU device otherwise. Allows overriding by providing an explicit device.

### Parameters
#### Parameters
- **x** (torch.Tensor | other type) - The tensor or object to get the device from.
- **device** (str | torch.device | None) - Optional. Device (as str or torch.device) to fall back to if x is not a tensor or does not have a device.

### Returns
- **torch.device** - A matching torch.device object.
```

--------------------------------

### Create Silhouette and Phong Renderers

Source: https://github.com/facebookresearch/pytorch3d/blob/main/docs/tutorials/camera_position_optimization_with_differentiable_rendering.ipynb

Sets up two mesh renderers: one for silhouette generation and another for Phong shading. Requires camera and blend parameters.

```python
raster_settings = RasterizationSettings(
    image_size=256, 
    blur_radius=np.log(1. / 1e-4 - 1.) * blend_params.sigma,
    faces_per_pixel=100,
)

# Create a silhouette mesh renderer by composing a rasterizer and a shader. 
silhouette_renderer = MeshRenderer(
    rasterizer=MeshRasterizer(
        cameras=cameras, 
        raster_settings=raster_settings
    ),
    shader=SoftSilhouetteShader(blend_params=blend_params)
)


# We will also create a Phong renderer. This is simpler and only needs to render one face per pixel.
raster_settings = RasterizationSettings(
    image_size=256, 
    blur_radius=0.0, 
    faces_per_pixel=1,
)
# We can add a point light in front of the object. 
lights = PointLights(device=device, location=((2.0, 2.0, -2.0),))
phong_renderer = MeshRenderer(
    rasterizer=MeshRasterizer(
        cameras=cameras, 
        raster_settings=raster_settings
    ),
    shader=HardPhongShader(device=device, cameras=cameras, lights=lights)
)
```