MRRA-GAN: Multi-Resolution Relation-Aware GAN for Point Cloud Completion

Point cloud completion has got increasingly attention recently. Its task is to predict a complete point cloud from a partial one, which plays a vital role in three-dimension technology. In order to better obtain the multi-level local information of the point cloud and better combine the local information with the global information for analysis, we proposed a novel Generative Adversarial Network(GAN) for point cloud completion, which called Multi-Resolution Relation-Aware GAN(MRRA-GAN). We designed a Multi-Resolution Key Points Generator(MKPG) which uses multi-resolution point cloud as input to construct key points, a Point Cloud Tree Generator(PTG) to construct Point Cloud Tree(PCT) and a penalty item called Uniformity Penalty(UP) to increase the uniformity of the output point cloud. Experiments, ablation study and robustness test demonstrate the effectiveness of our network, even chanllenging point cloud with different missing degree.