  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.
