The objective of this work is to infer the 3D shape of an object from a
single image. We use sculptures as our training and test bed, as these have
great variety in shape and appearance.
To achieve this we build on the success of multiple view geometry (MVG) which
is able to accurately provide correspondences between images of 3D objects
under varying viewpoint and illumination conditions, and make the following
contributions: first, we introduce a new loss function that can harness
image-to-image correspondences to provide a supervisory signal to train a deep
network to infer a depth map. The network is trained end-to-end by
differentiating through the camera. Second, we develop a processing pipeline to
automatically generate a large scale multi-view set of correspondences for
training the network. Finally, we demonstrate that we can indeed obtain a depth
map of a novel object from a single image for a variety of sculptures with
varying shape/texture, and that the network generalises at test time to new
domains (e.g. synthetic images).

本文的研究目的是从单个图像中推断出物体的三维形状，为了实现这一目标，本文使用雕塑作为实验数据集，并基于多视角几何（MVG）的成功经验，提出了一种新的损失函数，利用图像与图像之间的对应关系训练深度网络，完成端到端的单张图像深度估计任务。同时，为了自动地生成大规模的多视图对应数据集，本文提出了一种数据处理方法。在多种雕塑的数据集上的实验表明，本方法可以从单张图像中推断出新物体的三维形状，并且在测试时可以泛化到新的领域（如合成图像）中。