Computer Graphics Systems Group, Hasso-Plattner-Institut, University of Potsdam, Prof.-Dr.-Helmert-Str 2-3, 14482 Potsdam, Germany.
IEEE Trans Vis Comput Graph. 2013 May;19(5):866-85. doi: 10.1109/TVCG.2012.160.
This paper surveys the field of nonphotorealistic rendering (NPR), focusing on techniques for transforming 2D input (images and video) into artistically stylized renderings. We first present a taxonomy of the 2D NPR algorithms developed over the past two decades, structured according to the design characteristics and behavior of each technique. We then describe a chronology of development from the semiautomatic paint systems of the early nineties, through to the automated painterly rendering systems of the late nineties driven by image gradient analysis. Two complementary trends in the NPR literature are then addressed, with reference to our taxonomy. First, the fusion of higher level computer vision and NPR, illustrating the trends toward scene analysis to drive artistic abstraction and diversity of style. Second, the evolution of local processing approaches toward edge-aware filtering for real-time stylization of images and video. The survey then concludes with a discussion of open challenges for 2D NPR identified in recent NPR symposia, including topics such as user and aesthetic evaluation.
本文调查了非真实感渲染（NPR）领域，重点介绍了将 2D 输入（图像和视频）转换为艺术风格化渲染的技术。我们首先根据每种技术的设计特点和行为，呈现了过去二十年来开发的 2D NPR 算法的分类法。然后，我们描述了从 90 年代早期半自动绘画系统，到 90 年代后期由图像梯度分析驱动的自动绘画渲染系统的发展历程。然后，我们参考分类法讨论了 NPR 文献中的两个互补趋势。首先，将更高层次的计算机视觉与 NPR 融合，说明了朝着场景分析驱动艺术抽象和风格多样性的趋势。其次，探讨了从局部处理方法到边缘感知滤波的演变，以实现图像和视频的实时风格化。本文最后讨论了最近的 NPR 研讨会上确定的 2D NPR 的开放性挑战，包括用户和美学评估等主题。
