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TensorFlow Graphics: Differentiable Graphics Layers for TensorFlow

TensorFlow Graphics: Differentiable Graphics Layers for TensorFlow

TensorFlow Graphics

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.

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.

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.

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 documentation for
instructions on how to install TensorFlow Graphics.

API Documentation

You can find the API documentation here.

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
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

Intermediate

Advanced

TensorBoard 3D

Visual debugging is a great way to assess whether an experiment is going in the
right direction. To this end, TensorFlow Graphics comes with a TensorBoard
plugin to interactively visualize 3D meshes and point clouds. Follow these instructions
to install and configure TensorBoard 3D.

Coming next...

Among many things, we are hoping to release resamplers, additional 3D
convolution and pooling operators, and a differentiable rasterizer!

Follow us on Twitter to hear about the
latest updates!

Additional Information

You may use this software under the Apache 2.0 License.

Community

As part of TensorFlow, we're committed to fostering an open and welcoming
environment.

References

If you use TensorFlow Graphics in your research, please reference it as:

@inproceedings{TensorflowGraphicsIO2019,
   author = {Valentin, Julien and Keskin, Cem and Pidlypenskyi, Pavel and Makadia, Ameesh and Sud, Avneesh and Bouaziz, Sofien},
   title = {TensorFlow Graphics: Computer Graphics Meets Deep Learning},
   year = {2019}
}

Contributors - in alphabetical order

  • Sofien Bouaziz
  • Jay Busch
  • Forrester Cole
  • Ambrus Csaszar
  • Boyang Deng
  • Ariel Gordon
  • Cem Keskin
  • Ameesh Makadia
  • Rohit Pandey
  • Pavel Pidlypenskyi
  • Avneesh Sud
  • Anastasia Tkach
  • Julien Valentin
  • He Wang
  • Yinda Zhang

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