Skip to content

Latest commit

 

History

History
13 lines (8 loc) · 712 Bytes

README.md

File metadata and controls

13 lines (8 loc) · 712 Bytes

Applying deep learning algorithms to rendering and post-processing of 3D scenes

This project implements rendering of 3D indoor scenes using deep learning algorithms such as convolutional neural networks (CNN) and conditional generative adversarial networks (CGAN). The pix2pix model is used to generate the final frame render having a G-buffer as input.

result

The program receives 3 images as input:

  • depth buffer
  • normals
  • albedo

app