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Website and docs updates

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Summary:
- Added sbranson's fit mesh tutorial to the website
- Updated rendering docs with info about texturing and new shader types.

TODO:
- add pointcloud rendering tutorial to the website as well (https://github.com/facebookresearch/pytorch3d/blob/master/docs/tutorials/render_colored_points.ipynb)
- docs for camera
- update some tutorials which depended on the Textures from structures.

Reviewed By: gkioxari

Differential Revision: D23143977

fbshipit-source-id: 6843c9bf3ce11115c459c64da5b0ad778dc92177
This commit is contained in:
Nikhila Ravi
2020-08-21 19:16:07 -07:00
committed by Facebook GitHub Bot
parent 9a50cf800e
commit d330765847
13 changed files with 50 additions and 90 deletions
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docs/notes/datasets.md
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@@ -8,10 +8,8 @@ The PyTorch3D [ShapeNetCore data loader](https://github.com/facebookresearch/pyt
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.
### 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.