# TensorFlow Graphics
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The last few years have seen a rise in novel differentiable graphics layers
which can be inserted in neural network architectures. From spatial transformers
to differentiable graphics renderers, these new layers leverage the knowledge
acquired over years of computer vision and graphics research to build new and
more efficient network architectures. Explicitly modeling geometric priors and
constraints into neural networks opens up the door to architectures that can be
trained robustly, efficiently, and more importantly, in a self-supervised
fashion.
## Overview
At a high level, a computer graphics pipeline requires a representation of 3D
objects and their absolute positioning in the scene, a description of the
material they are made of, lights and a camera. This scene description is then
interpreted by a renderer to generate a synthetic rendering.
<div align="center">
<img border="0" src="https://storage.googleapis.com/tensorflow-graphics/git/readme/graphics.jpg" width="600">
</div>
In comparison, a computer vision system would start from an image and try to
infer the parameters of the scene. This allows the prediction of which objects
are in the scene, what materials they are made of, and their three-dimensional
position and orientation.
<div align="center">
<img border="0" src="https://storage.googleapis.com/tensorflow-graphics/git/readme/cv.jpg" width="600">
</div>
Training machine learning systems capable of solving these complex 3D vision
tasks most often requires large quantities of data. As labelling data is a
costly and complex process, it is important to have mechanisms to design
machine learning models that can comprehend the three dimensional world while
being trained without much supervision. Combining computer vision and computer
graphics techniques provides a unique opportunity to leverage the vast amounts
of readily available unlabelled data. As illustrated in the image below,
this can, for instance, be achieved using analysis by synthesis where the vision
system extracts the scene parameters and the graphics system
renders back an image based on them. If the rendering matches the original
image, the vision system has accurately extracted the scene parameters. In this
setup, computer vision and computer graphics go hand in hand, forming a single
machine learning system similar to an autoencoder, which can be trained in a
self-supervised manner.
<div align="center">
<img border="0" src="https://storage.googleapis.com/tensorflow-graphics/git/readme/cv_graphics.jpg" width="600">
</div>
Tensorflow Graphics is being developed to help tackle these types of challenges
and to do so, it provides a set of differentiable graphics and geometry layers
(e.g. cameras, reflectance models, spatial transformations, mesh convolutions)
and 3D viewer functionalities (e.g. 3D TensorBoard) that can be used to train
and debug your machine learning models of choice.
## Installing TensorFlow Graphics
See the [install](tensorflow_graphics/g3doc/install.md) documentation for
instructions on how to install TensorFlow Graphics.
## API Documentation
You can find the API documentation [here](https://github.com/tensorflow/graphics/blob/master/tensorflow_graphics/g3doc/api_docs/python/tfg.md).
## Compatibility
TensorFlow Graphics is fully compatible with the latest stable release of
TensorFlow, tf-nightly, and tf-nightly-2.0-preview. All the functions are
compatible with graph and eager execution.
## Debugging
Tensorflow Graphics heavily relies on L2 normalized tensors, as well as having
the inputs to specific function be in a pre-defined range. Checking for all of
this takes cycles, and hence is not activated by default. It is recommended to
turn these checks on during a couple epochs of training to make sure that
everything behaves as expected. This [page](https://github.com/tensorflow/graphics/blob/master/tensorflow_graphics/g3doc/debug_mode.md)
provides the instructions to enable these checks.
## Colab tutorials
To help you get started with some of the functionalities provided by TF
Graphics, some Colab notebooks are available below and roughly ordered by
difficulty. These Colabs touch upon a large range of topics including, object
pose estimation, interpolation, object materials, lighting, non-rigid
surface deformation, spherical harmonics, and mesh convolutions.
NOTE: the tutorials are maintained carefully. However, they are not considered
part of the API and they can change at any time without warning. It is not
advised to write code that takes dependency on them.
### Beginner
<div align="center">
<table>
<tr>
<th style="text-align:center"><a href="https://colab.research.google.com/github/tensorflow/graphics/blob/master/tensorflow_graphics/notebooks/6dof_alignment.ipynb">Object pose estimation</a></th>
<th style="text-align:center"><a href="https://colab.research.google.com/github/tensorflow/graphics/blob/master/tensorflow_graphics/notebooks/intrinsics_optimization.ipynb">Camera intrisic optimization</a></th>
</tr>
<tr>
<td align="center">
<a href="https://colab.research.google.com/github/tensorflow/graphics/blob/master/tensorflow_graphics/notebooks/6dof_alignment.ipynb"><img border="0" src="https://storage.googleapis.com/tensorflow-graphics/notebooks/6dof_pose/thumbnail.jpg" width="200" height="200">
</a>
</td>
<td align="center">
<a href="https://colab.research.google.com/github/tensorflow/graphics/blob/master/tensorflow_graphics/notebooks/intrinsics_optimization.ipynb"><img border="0" src="https://storage.googleapis.com/tensorflow-graphics/notebooks/intrinsics/intrinsics_thumbnail.png" width="200" height="200">
</a>
</td>
</tr>
</table>
</div>
### Intermediate
<div align="center">
<table>
<tr>
<th style="text-align:center"><a href="https://colab.research.google.com/github/tensorflow/graphics/blob/master/tensorflow_graphics/notebooks/interpolation.ipynb">B-spline and slerp interpolation</a></th>
<th style="text-align:center"><a href="https://colab.research.google.com/github/tensorflow/graphics/blob/master/tensorflow_graphics/notebooks/reflectance.ipynb">Reflectance</a></th>
<th style="text-align:center"><a href="https://colab.research.google.com/github/tensorflow/graphics/blob/master/tensorflow_graphics/notebooks/non_rigid_deformation.ipynb">Non-rigid surface deformation</a></th>
</tr>
<tr>
<td align="center"><a href="https://colab.research.google.com/github/tensorflow/graphics/blob/master/tensorflow_graphics/notebooks/interpolation.ipynb"><img border="0" src="https://storage.googleapis.com/tensorflow-graphics/notebooks/interpolation/thumbnail.png" width="200" height="200"> </td>
<td align="center"><a href="https://colab.research.google.com/github/tensorflow/graphics/blob/master/tensorflow_graphics/notebooks/reflectance.ipynb"><img border="0" src="https://storage.googleapis.com/tensorflow-graphics/notebooks/reflectance/thumbnail.png" width="200" height="200"></td>
<td align="center"><a href="https://colab.research.google.com/github/tensorflow/graphics/blob/master/tensorflow_graphics/notebooks/non_rigid_deformation.ipynb"><img border="0" src="https://storage.googleapis.com/tensorflow-graphics/notebooks/non_rigid_deformatio
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tensorflow-graphics-gpu-1.0.0.tar.gz (133个子文件)
setup.cfg 38B
README.md 12KB
PKG-INFO 763B
PKG-INFO 763B
graph_convolution_test.py 30KB
utils_test.py 29KB
quaternion_test.py 29KB
rotation_matrix_3d_test.py 25KB
quaternion.py 22KB
spherical_harmonics_test.py 21KB
graph_pooling_test.py 21KB
utils.py 19KB
graph_convolution.py 18KB
axis_angle_test.py 17KB
shape.py 16KB
spherical_harmonics.py 15KB
shape_test.py 14KB
rotation_matrix_3d.py 14KB
graph_pooling.py 14KB
asserts_test.py 13KB
normals_test.py 13KB
perspective_test.py 13KB
mesh_segmentation_dataio.py 13KB
test_case.py 12KB
euler_test.py 12KB
graph_convolution.py 12KB
quadratic_radial_distortion_test.py 12KB
perspective.py 12KB
axis_angle.py 12KB
graph_convolution_test.py 12KB
asserts.py 11KB
vector_test.py 11KB
slerp.py 11KB
safe_ops.py 10KB
bspline.py 10KB
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rotation_matrix_2d_test.py 10KB
phong_test.py 10KB
blinn_phong_test.py 10KB
ray_test.py 10KB
quadratic_radial_distortion.py 10KB
levenberg_marquardt.py 9KB
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normals.py 9KB
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as_conformal_as_possible.py 9KB
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mesh_viewer.py 3KB
rotation_matrix_common.py 3KB
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triangle.py 3KB
grid_test.py 3KB
srgb.py 3KB
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triangulated_stripe.py 2KB
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共 133 条
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