论文标题
共享人：结合无监督几何估计的合成和真实数据
SharinGAN: Combining Synthetic and Real Data for Unsupervised Geometry Estimation
论文作者
论文摘要
当训练网络从单个图像中确定几何信息时，我们提出了一种结合合成图像和真实图像的新方法。我们建议一种将两种图像类型映射到单个共享域中的方法。这连接到主要网络以进行端到端培训。理想情况下，这导致来自两个域的图像，这些域将共享信息呈现给主要网络。我们的实验表明，在两个重要领域的最新域，人脸的表面正常估计以及室外场景的单眼深度估计，均在无监督的环境中进行了显着改善。
We propose a novel method for combining synthetic and real images when training networks to determine geometric information from a single image. We suggest a method for mapping both image types into a single, shared domain. This is connected to a primary network for end-to-end training. Ideally, this results in images from two domains that present shared information to the primary network. Our experiments demonstrate significant improvements over the state-of-the-art in two important domains, surface normal estimation of human faces and monocular depth estimation for outdoor scenes, both in an unsupervised setting.
