Multi-Attribute Joint Point Cloud Super-Resolution with Adversarial Feature Graph Networks

3D point cloud super-resolution (PCSR) plays an important role in many applications, which can infer a dense geometric shape from a sparse one. However, existing PCSR methods only leverage the geometric properties to predict dense geometric coordinates without considering the importance of correlated attributes in the prediction of complex geometric structures. In this paper, we propose a novel PCSR network by leveraging color attributes to improve the reconstruction quality of dense geometric shape. In the proposed network, we utilize graph convolutions to obtain cross-domain structure representation for point cloud from both geometric coordinates and color attributes, which is constructed by aggregating local points based on the similarity of cross-domain features. Furthermore, we propose a shape-aware loss function to cooperate with network training, which constrains the point cloud generation from both overall and detailed aspects. Extensive experimental results show that our proposed method outperforms the state-of-the-art methods from both objective and subjective quality.