In computer vision, convolutional neural networks (CNNs) have recently
achieved new levels of performance for several inverse problems where RGB pixel
appearance is mapped to attributes such as positions, normals or reflectance.
In computer graphics, screen-space shading has recently increased the visual
quality in interactive image synthesis, where per-pixel attributes such as
positions, normals or reflectance of a virtual 3D scene are converted into RGB
pixel appearance, enabling effects like ambient occlusion, indirect light,
scattering, depth-of-field, motion blur, or anti-aliasing. In this paper we
consider the diagonal problem: synthesizing appearance from given per-pixel
attributes using a CNN. The resulting Deep Shading simulates various
screen-space effects at competitive quality and speed while not being
programmed by human experts but learned from example images.

本文介绍了一种基于卷积神经网络的 Deep Shading 方法，通过学习示例图像生成给定像素属性的外观，模拟了屏幕空间中的各种影响，提高了交互式图像合成的视觉质量和速度。