Estimating Coarse 3D Shape and Pose from the Bounding Contour

Single-view reconstruction of a smooth 3D object is an ill-posed problem. Surface cues such as shading and texture provide local constraints on shape, but these cues can be weak, making it a challenge to recover globally correct models. The bounding contour can play an important role in constraining this global integration. Here we focus in particular on information afforded by the overall elongation (aspect ratio) of the bounding contour. We hypothesize that the tendency of objects to be relatively compact and the generic view assumption together induce a statistical dependency between the observed elongation of the object boundary and the coarse 3D shape of the solid object, a dependency that could potentially be exploited by single-view methods. To test this hypothesis we assemble a new dataset of solid 3D shapes and study the joint statistics of ellipsoidal approximations to these shapes and elliptical approximations of their orthographically projected boundaries. Optimal estimators derived from these statistics confirm our hypothesis, and we show that these estimators can be used to generate coarse 3D shape-pose estimates from the bounding contour that are significantly and substantially superior to competing methods.