Projected Generative Adversarial Network for Point Cloud Completion

Abstract

Acquiring semantics directly from a point cloud is an important requirement for handling point cloud tasks. However, point clouds captured with laser scanner equipment are often incomplete due to the limitations posed by target occlusion and light reflection. Consequently, recovering the complete point clouds from partial and sparse ones is essential for further studies. In this paper, we model a novel projected generative adversarial network (PGAN) for point cloud completion. First, we present a multi-scale generator module (MSGM) to fully capture the local structures and global shape in the raw incompletion point cloud and generate the multi-scale complete point cloud. In contrast to existing point cloud feature extractors, our MSGM promotes a correlation between different regions of an incomplete point cloud and integrates the contextual information of the point cloud. Second, we observe that the existing point discriminator is inadequate to enhance the discrimination of the prediction point cloud. To address this problem, we project the completed point cloud to 2D maps and apply adversarial training to discriminate the geometrical shape from a specific viewpoint. Comprehensive experiments on the ShapeNet and ModelNet40 datasets show that the proposed method performs well against existing point cloud completion tasks. We also present an ablation study to demonstrate the advantages of the projected generative adversarial network.