The DOI matches its Crossref record; 52 of the 52 references it cites were found in a registry or an index.
PUFA-GAN: A Frequency-Aware Generative Adversarial Network for 3D Point Cloud Upsampling
Open accessAbstract
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.
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Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
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Densely Connected Convolutional Networks
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Unpaired image-to-image translation using cycle-consistent adversarial networks
Zhu, J.-Y., Park, T., Isola, P., & Efros, A. A. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.244 · Cited by 23,602
Image-to-Image Translation with Conditional Adversarial Networks
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Squeeze-and-Excitation Networks
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Cited by
(1996) · Institutional Research Information System University of Turin (University of Turin) · doi:10.1111/j · Cited by 5,486
Metadata
- Authors
- Liu, H., Yuan, H., Hou, J., Hamzaoui, R., & Gao, W.
- Venue
- IEEE Transactions on Image Processing
- Published
- 2022
- Volume
- 31
- Pages
- 7389-7402
- ISSN
- 1057-7149 (ISSN-L)
- 1941-0042
- OpenAlex
- W4310596280
- Cited by
- 339
- References
- 52
- FWCI
- 17.03
- Type
- journal article
- Language
- en
- Open access
- green
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