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Patch-Based Progressive 3D Point Set Upsampling
Open accessAbstract
We present a detail-driven deep neural network for point set upsampling. A high-resolution point set is essential for point-based rendering and surface reconstruction. Inspired by the recent success of neural image super-resolution techniques, we progressively train a cascade of patch-based upsampling networks on different levels of detail end-to-end. We propose a series of architectural design contributions that lead to a substantial performance boost. The effect of each technical contribution is demonstrated in an ablation study. Qualitative and quantitative experiments show that our method significantly outperforms the state-of-the-art learning-based and optimazation-based approaches, both in terms of handling low-resolution inputs and revealing high-fidelity details.
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Metadata
- Authors
- Yifan, W., Wu, S., Huang, H., Cohen-Or, D., & Sorkine-Hornung, O.
- Venue
- 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2019-06
- Pages
- 5951-5960
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2963390820
- Cited by
- 317
- References
- 59
- FWCI
- 42.78
- Type
- conference paper
- Language
- en
- Open access
- green
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