423A35C7/pytorch3d
1
0
Fork 0
mirror of https://github.com/facebookresearch/pytorch3d.git synced 2025-12-17 12:50:35 +08:00
Code Issues Packages Projects Releases Wiki Activity

Use a consistent case for PyTorch3D

Browse Source 
Summary: Use a consistent case for PyTorch3D (matching the logo...): replace all occurrences of PyTorch3d with PyTorch3D across the codebase (including documentation and notebooks)

Reviewed By: wanyenlo, gkioxari

Differential Revision: D20427546

fbshipit-source-id: 8c7697f51434c51e99b7fe271935932c72a1d9b9
This commit is contained in:
Patrick Labatut
2020-03-17 12:45:38 -07:00
committed by Facebook GitHub Bot
parent 5d3cc3569a
commit 25d2e2c8b7
20 changed files with 54 additions and 54 deletions
Download Patch File Download Diff File Expand all files Collapse all files

2
docs/tutorials/bundle_adjustment.ipynb
View File

@@ -248,7 +248,7 @@
"\n",
"**`calc_camera_distance`** compares a pair of cameras. This function is important as it defines the loss that we are minimizing. The method utilizes the `so3_relative_angle` function from the SO3 API.\n",
"\n",
"**`get_relative_camera`** computes the parameters of a relative camera that maps between a pair of absolute cameras. Here we utilize the `compose` and `inverse` class methods from the PyTorch3d Transforms API."
"**`get_relative_camera`** computes the parameters of a relative camera that maps between a pair of absolute cameras. Here we utilize the `compose` and `inverse` class methods from the PyTorch3D Transforms API."
]
},
{

8
docs/tutorials/camera_position_optimization_with_differentiable_rendering.ipynb
View File

@@ -119,7 +119,7 @@
"source": [
"## 1. Load the Obj\n",
"\n",
"We will load an obj file and create a **Meshes** object. **Meshes** is a unique datastructure provided in PyTorch3d for working with **batches of meshes of different sizes**. It has several useful class methods which are used in the rendering pipeline. "
"We will load an obj file and create a **Meshes** object. **Meshes** is a unique datastructure provided in PyTorch3D for working with **batches of meshes of different sizes**. It has several useful class methods which are used in the rendering pipeline. "
]
},
{
@@ -129,7 +129,7 @@
"id": "8d-oREfkrt_Z"
},
"source": [
"If you are running this notebook locally after cloning the PyTorch3d repository, the mesh will already be available. **If using Google Colab, fetch the mesh and save it at the path `data/`**:"
"If you are running this notebook locally after cloning the PyTorch3D repository, the mesh will already be available. **If using Google Colab, fetch the mesh and save it at the path `data/`**:"
]
},
{
@@ -202,7 +202,7 @@
"source": [
"### Create a renderer\n",
"\n",
"A **renderer** in PyTorch3d is composed of a **rasterizer** and a **shader** which each have a number of subcomponents such as a **camera** (orthgraphic/perspective). Here we initialize some of these components and use default values for the rest. \n",
"A **renderer** in PyTorch3D is composed of a **rasterizer** and a **shader** which each have a number of subcomponents such as a **camera** (orthgraphic/perspective). Here we initialize some of these components and use default values for the rest. \n",
"\n",
"For optimizing the camera position we will use a renderer which produces a **silhouette** of the object only and does not apply any **lighting** or **shading**. We will also initialize another renderer which applies full **phong shading** and use this for visualizing the outputs. "
]
@@ -817,7 +817,7 @@
"source": [
"## 5. Conclusion \n",
"\n",
"In this tutorial we learnt how to **load** a mesh from an obj file, initialize a PyTorch3d datastructure called **Meshes**, set up an **Renderer** consisting of a **Rasterizer** and a **Shader**, set up an optimization loop including a **Model** and a **loss function**, and run the optimization. "
"In this tutorial we learnt how to **load** a mesh from an obj file, initialize a PyTorch3D datastructure called **Meshes**, set up an **Renderer** consisting of a **Rasterizer** and a **Shader**, set up an optimization loop including a **Model** and a **loss function**, and run the optimization. "
]
}
],

6
docs/tutorials/deform_source_mesh_to_target_mesh.ipynb
View File

@@ -37,8 +37,8 @@
"We will cover: \n",
"\n",
"- How to **load a mesh** from an `.obj` file\n",
"- How to use the PyTorch3d **Meshes** datastructure\n",
"- How to use 4 different PyTorch3d **mesh loss functions**\n",
"- How to use the PyTorch3D **Meshes** datastructure\n",
"- How to use 4 different PyTorch3D **mesh loss functions**\n",
"- How to set up an **optimization loop**\n",
"\n",
"\n",
@@ -654,7 +654,7 @@
"source": [
"## 6. Conclusion \n",
"\n",
"In this tutorial we learnt how to load a mesh from an obj file, initialize a PyTorch3d datastructure called **Meshes**, set up an optimization loop and use four different PyTorch3d mesh loss functions. "
"In this tutorial we learnt how to load a mesh from an obj file, initialize a PyTorch3D datastructure called **Meshes**, set up an optimization loop and use four different PyTorch3D mesh loss functions. "
]
}
],

8
docs/tutorials/render_textured_meshes.ipynb
View File

@@ -173,7 +173,7 @@
"\n",
"Load an `.obj` file and it's associated `.mtl` file and create a **Textures** and **Meshes** object. \n",
"\n",
"**Meshes** is a unique datastructure provided in PyTorch3d for working with batches of meshes of different sizes. \n",
"**Meshes** is a unique datastructure provided in PyTorch3D for working with batches of meshes of different sizes. \n",
"\n",
"**Textures** is an auxillary datastructure for storing texture information about meshes. \n",
"\n",
@@ -287,7 +287,7 @@
"source": [
"## 2. Create a renderer\n",
"\n",
"A renderer in PyTorch3d is composed of a **rasterizer** and a **shader** which each have a number of subcomponents such as a **camera** (orthographic/perspective). Here we initialize some of these components and use default values for the rest.\n",
"A renderer in PyTorch3D is composed of a **rasterizer** and a **shader** which each have a number of subcomponents such as a **camera** (orthographic/perspective). Here we initialize some of these components and use default values for the rest.\n",
"\n",
"In this example we will first create a **renderer** which uses a **perspective camera**, a **point light** and applies **phong shading**. Then we learn how to vary different components using the modular API. "
]
@@ -545,7 +545,7 @@
"source": [
"## 6. Batched Rendering\n",
"\n",
"One of the core design choices of the PyTorch3d API is to suport **batched inputs for all components**. \n",
"One of the core design choices of the PyTorch3D API is to suport **batched inputs for all components**. \n",
"The renderer and associated components can take batched inputs and **render a batch of output images in one forward pass**. We will now use this feature to render the mesh from many different viewpoints.\n"
]
},
@@ -628,7 +628,7 @@
},
"source": [
"## 7. Conclusion\n",
"In this tutorial we learnt how to **load** a textured mesh from an obj file, initialize a PyTorch3d datastructure called **Meshes**, set up an **Renderer** consisting of a **Rasterizer** and a **Shader**, and modify several components of the rendering pipeline. "
"In this tutorial we learnt how to **load** a textured mesh from an obj file, initialize a PyTorch3D datastructure called **Meshes**, set up an **Renderer** consisting of a **Rasterizer** and a **Shader**, and modify several components of the rendering pipeline. "
]
}
],