  Point cloud completion is an indispensable task for recovering complete point
clouds due to incompleteness caused by occlusion, limited sensor resolution,
etc. The family of coarse-to-fine generation architectures has recently
exhibited great success in point cloud completion and gradually became
mainstream. In this work, we unveil one of the key ingredients behind these
methods: meticulously devised feature extraction operations with explicit
cross-resolution aggregation. We present Cross-Resolution Transformer that
efficiently performs cross-resolution aggregation with local attention
mechanisms. With the help of our recursive designs, the proposed operation can
capture more scales of features than common aggregation operations, which is
beneficial for capturing fine geometric characteristics. While prior
methodologies have ventured into various manifestations of inter-level
cross-resolution aggregation, the effectiveness of intra-level one and their
combination has not been analyzed. With unified designs, Cross-Resolution
Transformer can perform intra- or inter-level cross-resolution aggregation by
switching inputs. We integrate two forms of Cross-Resolution Transformers into
one up-sampling block for point generation, and following the coarse-to-fine
manner, we construct CRA-PCN to incrementally predict complete shapes with
stacked up-sampling blocks. Extensive experiments demonstrate that our method
outperforms state-of-the-art methods by a large margin on several widely used
benchmarks. Codes are available at https://github.com/EasyRy/CRA-PCN.
