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.

通过精心设计的特征提取操作和显式的分辨率聚合，我们揭示了点云补全方法背后的关键因素之一：跨分辨率聚合。我们提出了一种效率高的Cross-Resolution Transformer，能够捕捉比常规聚合操作更多尺度的特征，对于捕捉细微的几何特征有益处。我们将两种形式的Cross-Resolution Transformer集成到一个上采样模块中，用于点云生成，并且通过逐步上采样的方式，构建了CRA-PCN来逐步预测完整的形状。广泛的实验证明，我们的方法在几个广泛使用的基准测试上明显优于现有方法。

CRA-PCN：点云补全的内部和跨级别交叉分辨率Transformer