Detector

Algorithm

Residual

Linguistics

Philosophy

Theoretical computer science

Telecommunications

Computer science

Discriminator

Point cloud

Graph

Pattern recognition (psychology)

Mathematics

Image (mathematics)

Feature (linguistics)

Upsampling

Artificial intelligence

Computer vision

IEEE Transactions on Image Processing

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 corresponding high frequency points help the discriminator learn the global and high frequency properties of the point cloud. We also propose an identity distribution loss function to make sure that the upsampled points remain on the underlying surface of the input low resolution point cloud. To assess the regularity of the upsampled points in high frequency regions, we introduce two evaluation metrics. Objective and subjective results demonstrate that the visual quality of the upsampled point clouds generated by our method is better than that of the state-of-the-art methods.

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