  Accurately predicting the 3D shape of any arbitrary object in any pose from a
single image is a key goal of computer vision research. This is challenging as
it requires a model to learn a representation that can infer both the visible
and occluded portions of any object using a limited training set. A training
set that covers all possible object shapes is inherently infeasible. Such
learning-based approaches are inherently vulnerable to overfitting, and
successfully implementing them is a function of both the architecture design
and the training approach. We present an extensive investigation of factors
specific to architecture design, training, experiment design, and evaluation
that influence reconstruction performance and measurement. We show that our
proposed SDFNet achieves state-of-the-art performance on seen and unseen shapes
relative to existing methods GenRe and OccNet. We provide the first large-scale
evaluation of single image shape reconstruction to unseen objects. The source
code, data and trained models can be found on
https://github.com/rehg-lab/3DShapeGen.
