In this blog we will see the use of TensorFlow graphics on three dimensional objects. So what is computer graphics? Computer Graphics is a sub-field of computer science which studies methods for digitally synthesising and manipulating visual content.
Today Computer graphics is a core technology used in films, games, videos, phones, computer display etc. It has evolved overs the years, today you can make amazing beautiful scenes which are rendered realistically.
In the last few years, the novel differentiable graphics layers which can be inserted in neural network architectures has risen a quite. From spatial transformers to differentiable graphics renders, these new layers leverage the knowledge acquired over years of computer vision and graphics research to build new and more efficient network architectures.
Explicitly modelling 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.
This is where tensor-flow graphics comes to play. To handle an image and generate a synthetic rendering, it provides variety of tools
Now lets see some various graphical functionality available:
Transformation is one of the important key for graphical representation and training of images.
It helps to cover all the rotations over the image on the training dateset, thus helping in building a good neural network model. You can import the tensor-flow transformation function using the following command line:
import tensorflow_graphics.geometry.transformation as tfg_transformation
The tensor-flow graphics has a maths module, which helps you implement various maths routine like interpolation, Cartesian, factorial as needed. Its a very helpful module developed by tensor-flow that lets you use complex mathematical functions using a single line of command. You can use following command to import maths module:
3D Convolutions and pooling:
Convolutions and pooling has been an important aspect when it comes to training a dataset over images. It is easy to to do as their are uniform grid of pixels.
But to do it on irregular structures and depths, its a bit harder. This is where tensor-flow graphics come to play, it allows to classify on three dimensional space. The following command will let you use the convolution and pooling on three dimensional objects.
from tensorflow_graphics.notebooks import mesh_segmentation_dataio as dataio
TensorFlow graphics is a amazing tool to up-skill your graphical representation of images. It can train three dimensional models and predict with high accuracy. It is getting popular among people with more advances it will keep getting better.