DPG-Net: Densely progressive-growing network for point cloud completion

Abstract

This paper proposes a Densely Progressive-growing Network (DPG-Net) for 3D point cloud completion based on the Generative Adversarial Network (GAN). It aims to learn useful features from incomplete 3D objects to infer the complete geometric shape. Most methods usually acquire global features directly from the incomplete point cloud. However, the global feature often loses local information from the input point cloud. To solve this problem, we design a novel network named DPG-Net for point cloud completion by proposing a multi-resolution dense contextual feature mechanism, a progressive growth process, and combining the adversarial process. Firstly, we study a Multi-resolution Densely Contextual Encoder to infer the potential features with local information of the partial point cloud. The encoder can obtain the global information of the point cloud while fusing the local structure details. Secondly, we propose a Progressive Growth Decoder, which can make full use of global information, to gradually refine local areas and generate a complete shape. In addition, the discriminator is used to control the network for a realistic point cloud. Our model composed of the above modules can extract the features from incomplete point clouds, and then generate detailed complete point clouds. The performance (chamfer distance) of the proposed method is better than other methods on different datasets. The experiment proves the effectiveness of our method on point cloud completion.