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Jeremy Reizenstein 14bd5e28e8 provide cow dataset 
Summary: Make a dummy single-scene dataset using the code from generate_cow_renders (used in existing NeRF tutorials)

Reviewed By: kjchalup

Differential Revision: D38116910

fbshipit-source-id: 8db6df7098aa221c81d392e5cd21b0e67f65bd70
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data
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utils
provide cow dataset
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bundle_adjustment.ipynb
update tutorials for version 0.6.2
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camera_position_optimization_with_differentiable_rendering.ipynb
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dataloaders_ShapeNetCore_R2N2.ipynb
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deform_source_mesh_to_target_mesh.ipynb
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fit_simple_neural_radiance_field.ipynb
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README.md
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render_colored_points.ipynb
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render_densepose.ipynb
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README.md

Tutorial notebooks

For current versions of the tutorials, which correspond to the latest release, please look at this directory at the stable tag, namely at https://github.com/facebookresearch/pytorch3d/tree/stable/docs/tutorials .

There are links at the project homepage for opening these directly in colab.

They install PyTorch3D from pip, which should work inside a GPU colab notebook. If you need to install PyTorch3D from source inside colab, you can use

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'`

instead.

The versions of these tutorials on the main branch may need to use the latest PyTorch3D from the main branch. You may be able to install this from source with the same commands as above, but replacing the last line with !pip install 'git+https://github.com/facebookresearch/pytorch3d.git'.