  We aim to infer 3D shape and pose of object from a single image and propose a
learning-based approach that can train from unstructured image collections,
supervised by only segmentation outputs from off-the-shelf recognition systems
(i.e. 'shelf-supervised'). We first infer a volumetric representation in a
canonical frame, along with the camera pose. We enforce the representation
geometrically consistent with both appearance and masks, and also that the
synthesized novel views are indistinguishable from image collections. The
coarse volumetric prediction is then converted to a mesh-based representation,
which is further refined in the predicted camera frame. These two steps allow
both shape-pose factorization from image collections and per-instance
reconstruction in finer details. We examine the method on both synthetic and
real-world datasets and demonstrate its scalability on 50 categories in the
wild, an order of magnitude more classes than existing works.
