  Real-world point clouds usually suffer from incompleteness and display
different poses. While current point cloud completion methods excel in
reproducing complete point clouds with consistent poses as seen in the training
set, their performance tends to be unsatisfactory when handling point clouds
with diverse poses. We propose a network named Rotation-Invariant Completion
Network (RICNet), which consists of two parts: a Dual Pipeline Completion
Network (DPCNet) and an enhancing module. Firstly, DPCNet generates a coarse
complete point cloud. The feature extraction module of DPCNet can extract
consistent features, no matter if the input point cloud has undergone rotation
or translation. Subsequently, the enhancing module refines the fine-grained
details of the final generated point cloud. RICNet achieves better rotation
invariance in feature extraction and incorporates structural relationships in
man-made objects. To assess the performance of RICNet and existing methods on
point clouds with various poses, we applied random transformations to the point
clouds in the MVP dataset and conducted experiments on them. Our experiments
demonstrate that RICNet exhibits superior completion performance compared to
existing methods.
