Learning to reconstruct 3D shapes using 2D images is an active research topic, with benefits of not requir-
ing 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 pro-
vided 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 reconstruc-
tion accuracy with state-of-the-art with higher efficiency.