An Efficient Bidirectional Point Pyramid Attention Network for 3D Point Cloud Completion

Abstract

Point cloud completion is a necessary task in real-world applications of recovering a complete geometry from missing regions of 3D objects. Furthermore, model efficiency is of vital importance in computer vision. In this paper, we present an efficient encoder–decoder network that predicts missing point clouds on the basis of incomplete point clouds. There are several advantages to this approach. First, a Mixed Attention Module (MAM) was implemented to obtain the correlational information of points. Second, the proposed Bidirectional Point Pyramid Attention Network (BiPPAN) can achieve simple and fast multiscale feature fusion to capture important features. Lastly, the designed encoder–decoder framework comprises skip connections to capture long-distance dependencies and structural information. We can conclude from the results of the experiments that the proposed network is an efficient and effective method to accomplish point cloud completion tasks.