论文标题
自我监督的2D图像到3D形状翻译，并具有分离的表示形式
Self-Supervised 2D Image to 3D Shape Translation with Disentangled Representations
论文作者
论文摘要
我们提出一个框架，以在2D图像视图和3D对象形状之间翻译。深度学习的最新进展使我们能够从场景中学习结构感知的表示。但是，现有的文献假设成对的图像和3D形状可用于全面监督。在本文中，我们提出了SIST，一个自制图像来塑造完成三个任务的翻译框架：（i）从单个图像中重建3D形状； （ii）学习删除形状，外观和观点的表示； （iii）从这些独立因素中产生现实的RGB图像。与现有方法相反，我们的方法不需要图像形成对进行培训。取而代之的是，它使用来自同一对象类的未配对图像和形状数据集，并共同训练图像发生器和形状重建网络。我们的翻译方法取得了令人鼓舞的结果，可以用定量和定性术语与完全监督的方法相当。
We present a framework to translate between 2D image views and 3D object shapes. Recent progress in deep learning enabled us to learn structure-aware representations from a scene. However, the existing literature assumes that pairs of images and 3D shapes are available for training in full supervision. In this paper, we propose SIST, a Self-supervised Image to Shape Translation framework that fulfills three tasks: (i) reconstructing the 3D shape from a single image; (ii) learning disentangled representations for shape, appearance and viewpoint; and (iii) generating a realistic RGB image from these independent factors. In contrast to the existing approaches, our method does not require image-shape pairs for training. Instead, it uses unpaired image and shape datasets from the same object class and jointly trains image generator and shape reconstruction networks. Our translation method achieves promising results, comparable in quantitative and qualitative terms to the state-of-the-art achieved by fully-supervised methods.
