In this talk I will discuss two related problems in 3D reconstruction: (i) recovering the 3D shape of a temporally varying non-rigid 3D surface given a single video sequence and (ii) reconstructing different instances of the same object class category given a large collection of images from that category. In both cases we extract dense 3D shape information by analysing shape variation -- in one case of the same object instance over time and in the other across different instances of objects that belong to the same class.
First I will discuss the problem of dense capture of 3D non-rigid surfaces from a monocular video sequence. We take a purely model-free approach where no strong assumptions are made about the object we are looking at or the way it deforms. We apply low rank and spatial smoothness priors to obtain dense non-rigid models using a variational approach.
Second I will describe our recent approach to populating the Pascal VOC dataset with dense, per-object 3D reconstructions, bootstrapped from class labels, ground truth figure-ground segmentations and a small set of keypoint annotations. Our proposed algorithm first estimates camera viewpoint using rigid structure-from-motion, then reconstructs objects shapes by optimizing over visual hull proposals guided by loose within-class shape similarity assumptions.