Given partial objects and some complete ones as references, point cloud completion aims to recover authentic shapes. However, existing methods pay little attention to general shapes, which leads to the poor authenticity of completion results. Besides, the missing patterns are diverse in reality, but existing methods can only handle fixed ones, which means a poor generalization ability. Considering that a partial point cloud is a subset of the corresponding complete one, we regard them as different samples of the same distribution and propose Structure Retrieval based Point Completion Network (SRPCN). It first uses k-means clustering to extract structure points and disperses them into distributions, and then KL Divergence is used as a metric to find the complete structure point cloud that best matches the input in a database. Finally, a PCN-like decoder network is adopted to generate the final results based on the retrieved structure point clouds. As structure plays an important role in describing the general shape of an object and the proposed structure retrieval method is robust to missing patterns, experiments show that our method can generate more authentic results and has a stronger generalization ability.

翻译：将部分对象和部分完整对象作为参考, 点云的完成旨在恢复真实形状。 但是, 现有方法很少注意一般形状, 导致完成结果的真实性差。 此外, 缺失的形态在现实中是多种多样的, 但现有方法只能处理固定的形态, 意味着简单化能力差。 考虑到部分点云是相应完整云的子集, 我们把它们视为相同分布的不同样本, 并提议基于结构检索点完成网络( SRPCN ) 。 它首先使用 k- 手段集成来提取结构点并将其分散到分布中, 然后将 KL divergence 作为一种衡量标准, 以找到与数据库输入最匹配的完整结构点云。 最后, 采用了类似 PCN 的解码网络来产生基于回收的结构云的最终结果。 由于结构在描述一个对象的一般形状和拟议的结构检索方法对缺失模式具有很强的作用, 实验表明我们的方法可以产生更真实的结果, 并且更强的概括能力。