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 https://github.com/hrzhou2/seedformer.

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。

SeedFormer: 基于补丁种子的点云完整性修复与上采样变换器