Point cloud completion aims to infer the complete geometries for missing
regions of 3D objects from incomplete ones. Previous methods usually predict
the complete point cloud based on the global shape representation extracted
from the incomplete input. However, the global representation often suffers
from the information loss of structure details on local regions of incomplete
point cloud. To address this problem, we propose Skip-Attention Network
(SA-Net) for 3D point cloud completion. Our main contributions lie in the
following two-folds. First, we propose a skip-attention mechanism to
effectively exploit the local structure details of incomplete point clouds
during the inference of missing parts. The skip-attention mechanism selectively
conveys geometric information from the local regions of incomplete point clouds
for the generation of complete ones at different resolutions, where the
skip-attention reveals the completion process in an interpretable way. Second,
in order to fully utilize the selected geometric information encoded by
skip-attention mechanism at different resolutions, we propose a novel
structure-preserving decoder with hierarchical folding for complete shape
generation. The hierarchical folding preserves the structure of complete point
cloud generated in upper layer by progressively detailing the local regions,
using the skip-attentioned geometry at the same resolution. We conduct
comprehensive experiments on ShapeNet and KITTI datasets, which demonstrate
that the proposed SA-Net outperforms the state-of-the-art point cloud
completion methods.

本文提出了一种 Skip-Attention Network（SA-Net）算法，以增强点云补全任务中原始点云局部结构信息的提取过程，该算法通过 skip-attention 机制实现选取不同分辨率下不同局部结构信息的编码。实验表明，SA-Net 算法在形状完整性生成上的性能好于当前的措施。