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rename master branch to main

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Summary: Change doc references to master branch to its new name main.

Reviewed By: nikhilaravi

Differential Revision: D30303018

fbshipit-source-id: cfdbb207dfe3366de7e0ca759ed56f4b8dd894d1
This commit is contained in:
Jeremy Reizenstein
2021-08-16 04:05:20 -07:00
committed by Facebook GitHub Bot
parent 103da63393
commit b0dd0c8821
15 changed files with 40 additions and 40 deletions
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6
docs/notes/batching.md
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@@ -26,7 +26,7 @@ The need for different mesh batch modes is inherent to the way PyTorch operators
<img src="assets/meshrcnn.png" alt="meshrcnn" width="700" align="middle" />
[meshes]: https://github.com/facebookresearch/pytorch3d/blob/master/pytorch3d/structures/meshes.py
[graphconv]: https://github.com/facebookresearch/pytorch3d/blob/master/pytorch3d/ops/graph_conv.py
[vert_align]: https://github.com/facebookresearch/pytorch3d/blob/master/pytorch3d/ops/vert_align.py
[meshes]: https://github.com/facebookresearch/pytorch3d/blob/main/pytorch3d/structures/meshes.py
[graphconv]: https://github.com/facebookresearch/pytorch3d/blob/main/pytorch3d/ops/graph_conv.py
[vert_align]: https://github.com/facebookresearch/pytorch3d/blob/main/pytorch3d/ops/vert_align.py
[meshrcnn]: https://github.com/facebookresearch/meshrcnn

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docs/notes/cubify.md
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@@ -5,7 +5,7 @@ sidebar_label: Cubify
# Cubify
The [cubify operator](https://github.com/facebookresearch/pytorch3d/blob/master/pytorch3d/ops/cubify.py) converts an 3D occupancy grid of shape `BxDxHxW`, where `B` is the batch size, into a mesh instantiated as a [Meshes](https://github.com/facebookresearch/pytorch3d/blob/master/pytorch3d/structures/meshes.py) data structure of `B` elements. The operator replaces every occupied voxel (if its occupancy probability is greater than a user defined threshold) with a cuboid of 12 faces and 8 vertices. Shared vertices are merged, and internal faces are removed resulting in a **watertight** mesh.
The [cubify operator](https://github.com/facebookresearch/pytorch3d/blob/main/pytorch3d/ops/cubify.py) converts an 3D occupancy grid of shape `BxDxHxW`, where `B` is the batch size, into a mesh instantiated as a [Meshes](https://github.com/facebookresearch/pytorch3d/blob/main/pytorch3d/structures/meshes.py) data structure of `B` elements. The operator replaces every occupied voxel (if its occupancy probability is greater than a user defined threshold) with a cuboid of 12 faces and 8 vertices. Shared vertices are merged, and internal faces are removed resulting in a **watertight** mesh.
The operator provides three alignment modes {*topleft*, *corner*, *center*} which define the span of the mesh vertices with respect to the voxel grid. The alignment modes are described in the figure below for a 2D grid.

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docs/notes/datasets.md
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@@ -9,12 +9,12 @@ sidebar_label: Data loaders
ShapeNet is a dataset of 3D CAD models. ShapeNetCore is a subset of the ShapeNet dataset and can be downloaded from https://www.shapenet.org/. There are two versions ShapeNetCore: v1 (55 categories) and v2 (57 categories).
The PyTorch3D [ShapeNetCore data loader](https://github.com/facebookresearch/pytorch3d/blob/master/pytorch3d/datasets/shapenet/shapenet_core.py) inherits from `torch.utils.data.Dataset`. It takes the path where the ShapeNetCore dataset is stored locally and loads models in the dataset. The ShapeNetCore class loads and returns models with their `categories`, `model_ids`, `vertices` and `faces`. The `ShapeNetCore` data loader also has a customized `render` function that renders models by the specified `model_ids (List[int])`, `categories (List[str])` or `indices (List[int])` with PyTorch3D's differentiable renderer.
The PyTorch3D [ShapeNetCore data loader](https://github.com/facebookresearch/pytorch3d/blob/main/pytorch3d/datasets/shapenet/shapenet_core.py) inherits from `torch.utils.data.Dataset`. It takes the path where the ShapeNetCore dataset is stored locally and loads models in the dataset. The ShapeNetCore class loads and returns models with their `categories`, `model_ids`, `vertices` and `faces`. The `ShapeNetCore` data loader also has a customized `render` function that renders models by the specified `model_ids (List[int])`, `categories (List[str])` or `indices (List[int])` with PyTorch3D's differentiable renderer.
The loaded dataset can be passed to `torch.utils.data.DataLoader` with PyTorch3D's customized collate_fn: `collate_batched_meshes` from the `pytorch3d.dataset.utils` module. The `vertices` and `faces` of the models are used to construct a [Meshes](https://github.com/facebookresearch/pytorch3d/blob/master/pytorch3d/structures/meshes.py) object representing the batched meshes. This `Meshes` representation can be easily used with other ops and rendering in PyTorch3D.
The loaded dataset can be passed to `torch.utils.data.DataLoader` with PyTorch3D's customized collate_fn: `collate_batched_meshes` from the `pytorch3d.dataset.utils` module. The `vertices` and `faces` of the models are used to construct a [Meshes](https://github.com/facebookresearch/pytorch3d/blob/main/pytorch3d/structures/meshes.py) object representing the batched meshes. This `Meshes` representation can be easily used with other ops and rendering in PyTorch3D.
### R2N2
The R2N2 dataset contains 13 categories that are a subset of the ShapeNetCore v.1 dataset. The R2N2 dataset also contains its own 24 renderings of each object and voxelized models. The R2N2 Dataset can be downloaded following the instructions [here](http://3d-r2n2.stanford.edu/).
The PyTorch3D [R2N2 data loader](https://github.com/facebookresearch/pytorch3d/blob/master/pytorch3d/datasets/r2n2/r2n2.py) is initialized with the paths to the ShapeNet dataset, the R2N2 dataset and the splits file for R2N2. Just like `ShapeNetCore`, it can be passed to `torch.utils.data.DataLoader` with a customized collate_fn: `collate_batched_R2N2` from the `pytorch3d.dataset.r2n2.utils` module. It returns all the data that `ShapeNetCore` returns, and in addition, it returns the R2N2 renderings (24 views for each model) along with the camera calibration matrices and a voxel representation for each model. Similar to `ShapeNetCore`, it has a customized `render` function that supports rendering specified models with the PyTorch3D differentiable renderer. In addition, it supports rendering models with the same orientations as R2N2's original renderings.
The PyTorch3D [R2N2 data loader](https://github.com/facebookresearch/pytorch3d/blob/main/pytorch3d/datasets/r2n2/r2n2.py) is initialized with the paths to the ShapeNet dataset, the R2N2 dataset and the splits file for R2N2. Just like `ShapeNetCore`, it can be passed to `torch.utils.data.DataLoader` with a customized collate_fn: `collate_batched_R2N2` from the `pytorch3d.dataset.r2n2.utils` module. It returns all the data that `ShapeNetCore` returns, and in addition, it returns the R2N2 renderings (24 views for each model) along with the camera calibration matrices and a voxel representation for each model. Similar to `ShapeNetCore`, it has a customized `render` function that supports rendering specified models with the PyTorch3D differentiable renderer. In addition, it supports rendering models with the same orientations as R2N2's original renderings.

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docs/notes/renderer_getting_started.md
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@@ -74,7 +74,7 @@ Since v0.3, [pulsar](https://arxiv.org/abs/2004.07484) can be used as a backend
<img align="center" src="assets/pulsar_bm.png" width="300">
Pulsar's processing steps are tightly integrated CUDA kernels and do not work with custom `rasterizer` and `compositor` components. We provide two ways to use Pulsar: (1) there is a unified interface to match the PyTorch3D calling convention seamlessly. This is, for example, illustrated in the [point cloud tutorial](https://github.com/facebookresearch/pytorch3d/blob/master/docs/tutorials/render_colored_points.ipynb). (2) There is a direct interface available to the pulsar backend, which exposes the full functionality of the backend (including opacity, which is not yet available in PyTorch3D). Examples showing its use as well as the matching PyTorch3D interface code are available in [this folder](https://github.com/facebookresearch/pytorch3d/tree/master/docs/examples).
Pulsar's processing steps are tightly integrated CUDA kernels and do not work with custom `rasterizer` and `compositor` components. We provide two ways to use Pulsar: (1) there is a unified interface to match the PyTorch3D calling convention seamlessly. This is, for example, illustrated in the [point cloud tutorial](https://github.com/facebookresearch/pytorch3d/blob/main/docs/tutorials/render_colored_points.ipynb). (2) There is a direct interface available to the pulsar backend, which exposes the full functionality of the backend (including opacity, which is not yet available in PyTorch3D). Examples showing its use as well as the matching PyTorch3D interface code are available in [this folder](https://github.com/facebookresearch/pytorch3d/tree/master/docs/examples).
---
@@ -84,7 +84,7 @@ For mesh texturing we offer several options (in `pytorch3d/renderer/mesh/texturi
1. **Vertex Textures**: D dimensional textures for each vertex (for example an RGB color) which can be interpolated across the face. This can be represented as an `(N, V, D)` tensor. This is a fairly simple representation though and cannot model complex textures if the mesh faces are large.
2. **UV Textures**: vertex UV coordinates and **one** texture map for the whole mesh. For a point on a face with given barycentric coordinates, the face color can be computed by interpolating the vertex uv coordinates and then sampling from the texture map. This representation requires two tensors (UVs: `(N, V, 2), Texture map: `(N, H, W, 3)`), and is limited to only support one texture map per mesh.
3. **Face Textures**: In more complex cases such as ShapeNet meshes, there are multiple texture maps per mesh and some faces have texture while other do not. For these cases, a more flexible representation is a texture atlas, where each face is represented as an `(RxR)` texture map where R is the texture resolution. For a given point on the face, the texture value can be sampled from the per face texture map using the barycentric coordinates of the point. This representation requires one tensor of shape `(N, F, R, R, 3)`. This texturing method is inspired by the SoftRasterizer implementation. For more details refer to the [`make_material_atlas`](https://github.com/facebookresearch/pytorch3d/blob/master/pytorch3d/io/mtl_io.py#L123) and [`sample_textures`](https://github.com/facebookresearch/pytorch3d/blob/master/pytorch3d/renderer/mesh/textures.py#L452) functions. **NOTE:**: The `TexturesAtlas` texture sampling is only differentiable with respect to the texture atlas but not differentiable with respect to the barycentric coordinates.
3. **Face Textures**: In more complex cases such as ShapeNet meshes, there are multiple texture maps per mesh and some faces have texture while other do not. For these cases, a more flexible representation is a texture atlas, where each face is represented as an `(RxR)` texture map where R is the texture resolution. For a given point on the face, the texture value can be sampled from the per face texture map using the barycentric coordinates of the point. This representation requires one tensor of shape `(N, F, R, R, 3)`. This texturing method is inspired by the SoftRasterizer implementation. For more details refer to the [`make_material_atlas`](https://github.com/facebookresearch/pytorch3d/blob/main/pytorch3d/io/mtl_io.py#L123) and [`sample_textures`](https://github.com/facebookresearch/pytorch3d/blob/main/pytorch3d/renderer/mesh/textures.py#L452) functions. **NOTE:**: The `TexturesAtlas` texture sampling is only differentiable with respect to the texture atlas but not differentiable with respect to the barycentric coordinates.
<img src="assets/texturing.jpg" width="1000">