Surface-from-Gradients: An Approach Based on Discrete Geometry Processing

Wuyuan Xie, Yunbo Zhang, Charlie C. L. Wang, Ronald C.-K. Chung

CVPR 2014

doi:10.1109/CVPR.2014.282 /cvpr/2014/xie2014cvpr-surfacefromgradients/

Abstract

In this paper, we propose an efficient method to reconstruct surface-from-gradients (SfG). Our method is formulated under the framework of discrete geometry processing. Unlike the existing SfG approaches, we transfer the continuous reconstruction problem into a discrete space and efficiently solve the problem via a sequence of least-square optimization steps. Our discrete formulation brings three advantages: 1) the reconstruction preserves sharp-features, 2) sparse/incomplete set of gradients can be well handled, and 3) domains of computation can have irregular boundaries. Our formulation is direct and easy to implement, and the comparisons with state-of-the-arts show the effectiveness of our method.

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Cite

Text

Xie et al. "Surface-from-Gradients: An Approach Based on Discrete Geometry Processing." Conference on Computer Vision and Pattern Recognition, 2014. doi:10.1109/CVPR.2014.282

Markdown

[Xie et al. "Surface-from-Gradients: An Approach Based on Discrete Geometry Processing." Conference on Computer Vision and Pattern Recognition, 2014.](https://mlanthology.org/cvpr/2014/xie2014cvpr-surfacefromgradients/) doi:10.1109/CVPR.2014.282

BibTeX

@inproceedings{xie2014cvpr-surfacefromgradients,
  title     = {{Surface-from-Gradients: An Approach Based on Discrete Geometry Processing}},
  author    = {Xie, Wuyuan and Zhang, Yunbo and Wang, Charlie C. L. and Chung, Ronald C.-K.},
  booktitle = {Conference on Computer Vision and Pattern Recognition},
  year      = {2014},
  doi       = {10.1109/CVPR.2014.282},
  url       = {https://mlanthology.org/cvpr/2014/xie2014cvpr-surfacefromgradients/}
}