  We propose a novel approach for 3D shape completion by synthesizing
multi-view depth maps. While previous work for shape completion relies on
volumetric representations, meshes, or point clouds, we propose to use
multi-view depth maps from a set of fixed viewing angles as our shape
representation. This allows us to be free of the limitations of memory for
volumetric representations and point clouds by casting shape completion into an
image-to-image translation problem. Specifically, we render depth maps of the
incomplete shape from a fixed set of viewpoints, and perform depth map
completion in each view. Different from image-to-image translation network that
completes each view separately, our novel network, multi-view completion net
(MVCN), leverages information from all views of a 3D shape to help the
completion of each single view. This enables MVCN to leverage more information
from different depth views to achieve high accuracy in single depth view
completion and keep the consistency among the completed depth images in
different views. Benefited by the multi-view representation and the novel
network structure, MVCN significantly improves the accuracy of 3D shape
completion in large-scale benchmarks compared to the state of the art.
