  Learning to reconstruct 3D shapes using 2D images is an active research
topic, with benefits of not requiring expensive 3D data. However, most work in
this direction requires multi-view images for each object instance as training
supervision, which oftentimes does not apply in practice. In this paper, we
relax the common multi-view assumption and explore a more challenging yet more
realistic setup of learning 3D shape from only single-view images. The major
difficulty lies in insufficient constraints that can be provided by single view
images, which leads to the problem of pose entanglement in learned shape space.
As a result, reconstructed shapes vary along input pose and have poor accuracy.
We address this problem by taking a novel domain adaptation perspective, and
propose an effective adversarial domain confusion method to learn
pose-disentangled compact shape space. Experiments on single-view
reconstruction show effectiveness in solving pose entanglement, and the
proposed method achieves on-par reconstruction accuracy with state-of-the-art
with higher efficiency.
