  In this paper the argument is made that for true novel view synthesis of
objects, where the object can be synthesized from any viewpoint, an explicit 3D
shape representation isdesired. Our method estimates point clouds to capture
the geometry of the object, which can be freely rotated into the desired view
and then projected into a new image. This image, however, is sparse by nature
and hence this coarse view is used as the input of an image completion network
to obtain the dense target view. The point cloud is obtained using the
predicted pixel-wise depth map, estimated from a single RGB input
image,combined with the camera intrinsics. By using forward warping and
backward warpingbetween the input view and the target view, the network can be
trained end-to-end without supervision on depth. The benefit of using point
clouds as an explicit 3D shape for novel view synthesis is experimentally
validated on the 3D ShapeNet benchmark. Source code and data will be available
at https://lhoangan.github.io/pc4novis/.
