  How will you repair a physical object with large missings? You may first
recover its global yet coarse shape and stepwise increase its local details. We
are motivated to imitate the above physical repair procedure to address the
point cloud completion task. We propose a novel stepwise point cloud completion
network (SPCNet) for various 3D models with large missings. SPCNet has a
hierarchical bottom-to-up network architecture. It fulfills shape completion in
an iterative manner, which 1) first infers the global feature of the coarse
result; 2) then infers the local feature with the aid of global feature; and 3)
finally infers the detailed result with the help of local feature and coarse
result. Beyond the wisdom of simulating the physical repair, we newly design a
cycle loss %based training strategy to enhance the generalization and
robustness of SPCNet. Extensive experiments clearly show the superiority of our
SPCNet over the state-of-the-art methods on 3D point clouds with large
missings.
