Implementation of Nalbach et al. 2017 paper.

Last update: Sep 08, 2022

Overview

Deep Shading

Convolutional Neural Networks for Screen-Space Shading

Our project is based on Nalbach et al. 2017 paper. In this project, a set of buffers are provided to a Deep Convolutional Network in order to synthetize differend shading effects (such as Ambient Occlusion, Depth of Field, Global Illumination and Sub-surface Scattering). The set of buffers depends os the shading effect we want to synthetize.

Input Bufers:

Result (Ambient Occlusion):

Requirements for the project

Python 3.x
Tensorflow 1.10
Keras
OpenCV 3.4(for loading,resizing images)
h5py(for saving trained model)
pyexr

Steps to run the repo

Clone the repo
Download the dataset (http://deep-shading-datasets.mpi-inf.mpg.de/)
Install the requirements
Generate the .tfrecord for trainning and validation (Use the DataReader.py)
Run "Shading.py"

References

[1] Oliver Nalbach, Elena Arabadzhiyska, Dushyant Mehta, Hans-Peter Seidel, Tobias Ritschel Deep Shading: Convolutional Neural Networks for Screen-Space Shading to appear in Proc. EGSR 2017

Implementation of Nalbach et al. 2017 paper.

Related tags

Overview

Deep Shading

Convolutional Neural Networks for Screen-Space Shading

Requirements for the project

Steps to run the repo

References

Owner

Marcel Santana

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