- publication
- CVPR 2019
- authors
- Wang Yifan, Shihao Wu, Hui Huang, Daniel Cohen-Or, Olga Sorkine-Hornung
We develop a deep neural network for 3D point set upsampling. Intuitively, our network learns different levels of detail in multiple steps, where each step focuses on a local patch from the output of the previous step. By progressively training our patch-based network end-to-end, we successfully upsample a sparse set of input points, step by step, to a dense point set with rich geometric details. Here we use circle plates for points rendering, which are color-coded by point normals.
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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acknowledgments
We thank the anonymous reviewers for their constructive comments and the SketchFab community for sharing their 3D models. This work was supported in parts by SNF grant 200021_162958, ISF grant 2366/16, NSFC (61761146002), LHTD (20170003), and the National Engineering Laboratory for Big Data System Computing Technology.