  Inferring the stereo structure of objects in the real world is a challenging
yet practical task. To equip deep models with this ability usually requires
abundant 3D supervision which is hard to acquire. It is promising that we can
simply benefit from synthetic data, where pairwise ground-truth is easy to
access. Nevertheless, the domain gaps are nontrivial considering the variant
texture, shape and context. To overcome these difficulties, we propose a
Visio-Perceptual Adaptive Network for single-view 3D reconstruction, dubbed
VPAN. To generalize the model towards a real scenario, we propose to fulfill
several aspects: (1) Look: visually incorporate spatial structure from the
single view to enhance the expressiveness of representation; (2) Cast:
perceptually align the 2D image features to the 3D shape priors with
cross-modal semantic contrastive mapping; (3) Mold: reconstruct stereo-shape of
target by transforming embeddings into the desired manifold. Extensive
experiments on several benchmarks demonstrate the effectiveness and robustness
of the proposed method in learning the 3D shape manifold from synthetic data
via a single-view. The proposed method outperforms state-of-the-arts on Pix3D
dataset with IoU 0.292 and CD 0.108, and reaches IoU 0.329 and CD 0.104 on
Pascal 3D+.
