  In this paper, we propose an object reconstruction apparatus that uses the
so-called Generic Primitives (GP) to complete shapes. A GP is a 3D point cloud
depicting a generalized shape of a class of objects. To reconstruct the objects
in a scene we first fit a GP onto each occluded object to obtain an initial raw
structure. Secondly, we use a model-based deformation technique to fold the
surface of the GP over the occluded object. The deformation model is encoded
within the layers of a Deep Neural Network (DNN), coined GFPNet. The objective
of the network is to transfer the particularities of the object from the scene
to the raw volume represented by the GP. We show that GFPNet competes with
state of the art shape completion methods by providing performance results on
the ModelNet and KITTI benchmarking datasets.
