  One major challenge in 3D reconstruction is to infer the complete shape
geometry from partial foreground occlusions. In this paper, we propose a method
to reconstruct the complete 3D shape of an object from a single RGB image, with
robustness to occlusion. Given the image and a silhouette of the visible
region, our approach completes the silhouette of the occluded region and then
generates a point cloud. We show improvements for reconstruction of
non-occluded and partially occluded objects by providing the predicted complete
silhouette as guidance. We also improve state-of-the-art for 3D shape
prediction with a 2D reprojection loss from multiple synthetic views and a
surface-based smoothing and refinement step. Experiments demonstrate the
efficacy of our approach both quantitatively and qualitatively on synthetic and
real scene datasets.
