ML Anthology

Patch-Based Progressive 3D Point Set Upsampling

Wang Yifan, Shihao Wu, Hui Huang, Daniel Cohen-Or, Olga Sorkine-Hornung
CVPR 2019
doi:10.1109/CVPR.2019.00611 /cvpr/2019/yifan2019cvpr-patchbased/

Abstract

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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Text

Yifan et al. "Patch-Based Progressive 3D Point Set Upsampling." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019. doi:10.1109/CVPR.2019.00611

Markdown

[Yifan et al. "Patch-Based Progressive 3D Point Set Upsampling." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019.](https://mlanthology.org/cvpr/2019/yifan2019cvpr-patchbased/) doi:10.1109/CVPR.2019.00611

BibTeX

@inproceedings{yifan2019cvpr-patchbased,
  title     = {{Patch-Based Progressive 3D Point Set Upsampling}},
  author    = {Yifan, Wang and Wu, Shihao and Huang, Hui and Cohen-Or, Daniel and Sorkine-Hornung, Olga},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2019},
  doi       = {10.1109/CVPR.2019.00611},
  url       = {https://mlanthology.org/cvpr/2019/yifan2019cvpr-patchbased/}
}