Accelerated Detail-Enhanced Ambient Occlusion

Ambient Occlusion (AO) is a technique to approximate the effect of environment lighting and add realism to a scene by accentuating surface details and adding soft shadows, which is widely used in multimedia applications. Neural Network Ambient Occlusion (NNAO) is a pioneer in introducing deep learning to accurate and real-time AO, but it has two limitations: 1) performance bottleneck under excessive amount of samples ; 2) low contrast and blurred edges leading to unreal effect. To overcome these two limitations, we propose Accelerated Detail-enhanced Ambient Occlusion (ADAO) method based on NNAO by adopting three image processing methods: 1) spiral sampling in screen space; 2) contrast enhancement of AO map; 3) normal-depth edge preserving bilateral filtering. Experimental results show that the proposed method is over 2 times faster than NNAO and produces shadows with more realistic details.