论文标题
来自单个图像的180度支出
180-degree Outpainting from a Single Image
论文作者
论文摘要
向观众的外围视觉展示上下文图像是增强沉浸式视觉体验的最有效技术之一。但是，大多数图像仅显示狭窄的视图，因为标准相机的视野（FOV）很小。为了克服这一局限性，我们提出了一种深度学习方法，该方法学会从狭窄视图图像中预测180°全景图像。具体而言，我们设计了一个杂乱的框架，该框架应用于近乎外围和中期区域的不同策略。两个网络分别训练，然后共同使用依次执行狭窄到90°的生成，并产生90°至180°。然后，生成的输出与其对齐输入融合，以产生扩展的等equiretectular图像以进行查看。我们的实验结果表明，使用深度学习的单视图对培训图像产生既可行又有前途。
Presenting context images to a viewer's peripheral vision is one of the most effective techniques to enhance immersive visual experiences. However, most images only present a narrow view, since the field-of-view (FoV) of standard cameras is small. To overcome this limitation, we propose a deep learning approach that learns to predict a 180° panoramic image from a narrow-view image. Specifically, we design a foveated framework that applies different strategies on near-periphery and mid-periphery regions. Two networks are trained separately, and then are employed jointly to sequentially perform narrow-to-90° generation and 90°-to-180° generation. The generated outputs are then fused with their aligned inputs to produce expanded equirectangular images for viewing. Our experimental results show that single-view-to-panoramic image generation using deep learning is both feasible and promising.
