PUFA-GAN: A Frequency-Aware Generative Adversarial Network for 3D Point Cloud Upsampling

We propose a generative adversarial network for point cloud upsampling, which can not only make the upsampled points evenly distributed on the underlying surface but also efficiently generate clean high frequency regions. The generator of our network includes a dynamic graph hierarchical residual aggregation unit and a hierarchical residual aggregation unit for point feature extraction and upsampling, respectively. The former extracts multiscale point-wise descriptive features, while the latter captures rich feature details with hierarchical residuals. To generate neat edges, our discriminator uses a graph filter to extract and retain high frequency points. The generated high resolution point cloud and…

• NN
  National Natural Science Foundation of China

  Awards: 62172259, 61871342, 62222110
• RG
  Research Grants Council, University Grants Committee

  Awards: CityU11219422, CityU11202320
• NS
  Natural Science Foundation of Shandong Province

  Award: ZR2022ZD38
• TS
  Taishan Scholar Project of Shandong Province

  Award: tsqn202103001