Point cloud completion via trigonometric encoding and self-attention-based feature fusion

Recovering a complete point cloud from a partial point cloud is a critical and challenging task for many 3D applications. In this paper, a point cloud completion network is proposed that focuses on improving the point cloud feature extraction and the initial generated point cloud in the encoding phase. The local details of the original point cloud are highlighted by introducing trigonometric positional embedding for point cloud encoding. Moreover, a self-attention mechanism for feature fusion is proposed to facilitate the generation of a complete point cloud. The experiments on multiple public datasets demonstrate that our network effectively achieves 3D point cloud completion with strong generalization, outperforming recent point cloud completion methods.