论文标题
学习分解和重新确定城市
Learning to Factorize and Relight a City
论文作者
论文摘要
我们提出了一个基于学习的框架，用于将户外场景分解为暂时变化的照明和永久场景因素。受经典固有图像分解的启发，我们的学习信号基于两个见解：1）结合分离的因素应重建原始图像，以及2）永久因素应在同一场景的多个时间样本中保持恒定。为了促进培训，我们从Google Street View组装了一个城市规模的户外时间幻想图像数据集，那里随着时间的推移，反复捕获相同的位置。该数据代表了时空户外图像的前所未有的规模。我们表明，我们学到的分离因素可用于以现实的方式操纵新型图像，例如改变照明效果和场景几何形状。请访问fixsize-a-city.github.io以获取动画结果。
We propose a learning-based framework for disentangling outdoor scenes into temporally-varying illumination and permanent scene factors. Inspired by the classic intrinsic image decomposition, our learning signal builds upon two insights: 1) combining the disentangled factors should reconstruct the original image, and 2) the permanent factors should stay constant across multiple temporal samples of the same scene. To facilitate training, we assemble a city-scale dataset of outdoor timelapse imagery from Google Street View, where the same locations are captured repeatedly through time. This data represents an unprecedented scale of spatio-temporal outdoor imagery. We show that our learned disentangled factors can be used to manipulate novel images in realistic ways, such as changing lighting effects and scene geometry. Please visit factorize-a-city.github.io for animated results.
