  Completing an unordered partial point cloud is a challenging task. Existing
approaches that rely on decoding a latent feature to recover the complete
shape, often lead to the completed point cloud being over-smoothing, losing
details, and noisy. Instead of decoding a whole shape, we propose to decode and
refine a low-resolution (low-res) point cloud first, and then performs a
patch-wise noise-aware upsampling rather than interpolating the whole sparse
point cloud at once, which tends to lose details. Regarding the possibility of
lacking details of the initially decoded low-res point cloud, we propose an
iterative refinement to recover the geometric details and a symmetrization
process to preserve the trustworthy information from the input partial point
cloud. After obtaining a sparse and complete point cloud, we propose a
patch-wise upsampling strategy. Patch-based upsampling allows to better recover
fine details unlike decoding a whole shape, however, the existing upsampling
methods are not applicable to completion task due to the data discrepancy
(i.e., input sparse data here is not from ground-truth). Therefore, we propose
a patch extraction approach to generate training patch pairs between the sparse
and ground-truth point clouds, and an outlier removal step to suppress the
noisy points from the sparse point cloud. Together with the low-res recovery,
our whole method is able to achieve high-fidelity point cloud completion.
Comprehensive evaluations are provided to demonstrate the effectiveness of the
proposed method and its individual components.
