  While data has certainly taken the center stage in computer vision in recent
years, it can still be difficult to obtain in certain scenarios. In particular,
acquiring ground truth 3D shapes of objects pictured in 2D images remains a
challenging feat and this has hampered progress in recognition-based object
reconstruction from a single image. Here we propose to bypass previous
solutions such as 3D scanning or manual design, that scale poorly, and instead
populate object category detection datasets semi-automatically with dense,
per-object 3D reconstructions, bootstrapped from:(i) class labels, (ii) ground
truth figure-ground segmentations and (iii) a small set of keypoint
annotations. Our proposed algorithm first estimates camera viewpoint using
rigid structure-from-motion and then reconstructs object shapes by optimizing
over visual hull proposals guided by loose within-class shape similarity
assumptions. The visual hull sampling process attempts to intersect an object's
projection cone with the cones of minimal subsets of other similar objects
among those pictured from certain vantage points. We show that our method is
able to produce convincing per-object 3D reconstructions and to accurately
estimate cameras viewpoints on one of the most challenging existing
object-category detection datasets, PASCAL VOC. We hope that our results will
re-stimulate interest on joint object recognition and 3D reconstruction from a
single image.
