Neural network ambient occlusion

We present Neural Network Ambient Occlusion (NNAO), a fast, accurate screen space ambient occlusion algorithm that uses a neural network to learn an optimal approximation of the ambient occlusion effect. Our network is carefully designed such that it can be computed in a single pass allowing it to be used as a drop-in replacement for existing screen space ambient occlusion techniques.

[1]  Peiran REN,et al.  Image based relighting using neural networks , 2015, ACM Trans. Graph..

[2]  Pradeep Sen,et al.  A machine learning approach for filtering Monte Carlo noise , 2015, ACM Trans. Graph..

[3]  Wojciech Matusik,et al.  CG2Real: Improving the Realism of Computer Generated Images Using a Large Collection of Photographs , 2011, IEEE Transactions on Visualization and Computer Graphics.

[4]  Martin Mittring,et al.  Finding next gen: CryEngine 2 , 2007, SIGGRAPH Courses.

[5]  Dominic Filion,et al.  Effects & techniques , 2008, SIGGRAPH '08.

[6]  Jian Sun,et al.  Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[7]  Razvan Pascanu,et al.  Theano: A CPU and GPU Math Compiler in Python , 2010, SciPy.

[8]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[9]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[10]  Paul Oliver.  Unreal Engine 4 Elemental , 2012, SIGGRAPH '12.

[11]  Louis Bavoil,et al.  Image-space horizon-based ambient occlusion , 2008, SIGGRAPH '08.

[12]  Morgan McGuire,et al.  The alchemy screen-space ambient obscurance algorithm , 2011, HPG '11.