The rapid development of point cloud learning has driven point cloud
completion into a new era. However, the information flows of most existing
completion methods are solely feedforward, and high-level information is rarely
reused to improve low-level feature learning. To this end, we propose a novel
Feedback Network (FBNet) for point cloud completion, in which present features
are efficiently refined by rerouting subsequent fine-grained ones. Firstly,
partial inputs are fed to a Hierarchical Graph-based Network (HGNet) to
generate coarse shapes. Then, we cascade several Feedback-Aware Completion
(FBAC) Blocks and unfold them across time recurrently. Feedback connections
between two adjacent time steps exploit fine-grained features to improve
present shape generations. The main challenge of building feedback connections
is the dimension mismatching between present and subsequent features. To
address this, the elaborately designed point Cross Transformer exploits
efficient information from feedback features via cross attention strategy and
then refines present features with the enhanced feedback features. Quantitative
and qualitative experiments on several datasets demonstrate the superiority of
proposed FBNet compared to state-of-the-art methods on point completion task.

本文提出了一种新的反馈网络（FBNet）用于点云完整性修复，通过用细粒度特征改进粗略形状的生成，并利用交叉注意力策略来修正现有的特征。在多个数据集上进行的定量和定性实验表明，FBNet 在点云修复任务上的性能比现有最先进的方法更优。