We present RGB2AO, a novel task to generate ambient occlusion (AO) from a single RGB image instead of screen space buffers such as depth and normal. RGB2AO produces a new image filter that creates a non‐directional shading effect that darkens enclosed and sheltered areas. RGB2AO aims to enhance two 2D image editing applications: image composition and geometry‐aware contrast enhancement. We first collect a synthetic dataset consisting of pairs of RGB images and AO maps. Subsequently, we propose a model for RGB2AO by supervised learning of a convolutional neural network (CNN), considering 3D geometry of the input image. Experimental results quantitatively and qualitatively demonstrate the effectiveness of our model.

中文翻译：

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RGB2AO：从 RGB 图像生成环境光遮挡

我们提出了 RGB2AO，这是一项从单个 RGB 图像而不是屏幕空间缓冲区（例如深度和法线）生成环境遮挡 (AO) 的新任务。RGB2AO 产生一个新的图像过滤器，它创建一个非定向阴影效果，使封闭和遮蔽区域变暗。RGB2AO 旨在增强两个 2D 图像编辑应用程序：图像合成和几何感知对比度增强。我们首先收集由成对的 RGB 图像和 AO 地图组成的合成数据集。随后，考虑到输入图像的 3D 几何形状，我们通过卷积神经网络 (CNN) 的监督学习提出了 RGB2AO 模型。实验结果定量和定性地证明了我们模型的有效性。