Convolutional networks for single-view object reconstruction have shown impressive performance and have become a popular subject of research. All existing techniques are united by the idea of having an encoder-decoder network that performs non-trivial reasoning about the 3D structure of the output space. In this work, we set up two alternative approaches that perform image classification and retrieval respectively. These simple baselines yield better results than state-of-the-art methods, both qualitatively and quantitatively. We show that encoder-decoder methods are statistically indistinguishable from these baselines, thus indicating that the current state of the art in single-view object reconstruction does not actually perform reconstruction but image classification. We identify aspects of popular experimental procedures that elicit this behavior and discuss ways to improve the current state of research.

该研究提出了两种替代方法进行单视图对象重建，并证明了编码器-解码器方法与这些基线方法在统计上是无法区分的，从而表明当前单视图对象重建技术实际上是在进行图像分类，而非重建。同时，研究者还发现了实验程序中存在的一些问题，并提出了改进方法。

单视角三维重建网络学习什么？