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

Novel View Synthesis from Single Images via Point Cloud Transformation