@inproceedings{975d35656992447795faa68a87460d00,
title = "Patch-based progressive 3D point set upsampling",
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.",
keywords = "3D from Multiview and Sensors, Deep Learning, Vision + Graphics",
author = "Wang Yifan and Shihao Wu and Hui Huang and Daniel Cohen-Or and Olga Sorkine-Hornung",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; null ; Conference date: 16-06-2019 Through 20-06-2019",
year = "2019",
month = jun,
doi = "10.1109/CVPR.2019.00611",
language = "אנגלית",
series = "Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition",
publisher = "IEEE Computer Society",
pages = "5951--5960",
booktitle = "Proceedings - 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019",
address = "ארצות הברית",
}