Point cloud completion has become increasingly popular among generation tasks
of 3D point clouds, as it is a challenging yet indispensable problem to recover
the complete shape of a 3D object from its partial observation. In this paper,
we propose a novel SeedFormer to improve the ability of detail preservation and
recovery in point cloud completion. Unlike previous methods based on a global
feature vector, we introduce a new shape representation, namely Patch Seeds,
which not only captures general structures from partial inputs but also
preserves regional information of local patterns. Then, by integrating seed
features into the generation process, we can recover faithful details for
complete point clouds in a coarse-to-fine manner. Moreover, we devise an
Upsample Transformer by extending the transformer structure into basic
operations of point generators, which effectively incorporates spatial and
semantic relationships between neighboring points. Qualitative and quantitative
evaluations demonstrate that our method outperforms state-of-the-art completion
networks on several benchmark datasets. Our code is available at
this https URL

本文介绍了一种名为 SeedFormer 的新型 Point cloud completion 方法，使用 Patch Seeds 来提高细节保留和恢复能力，并通过 Upsample Transformer 将 transformer 结构扩展到基本操作中，以实现粗到细的点云补全。定性和定量评估表明，该方法优于几个基准数据集上的现有补全网络。