Geometric Modeling and Processingmathscidoc:1608.16114
Distinguished Paper Award in 2018
2016
Man-made objects usually exhibit descriptive curved features (i.e., curve networks). The curve network of an object conveys
its high-level geometric and topological structure. We present a framework for extracting feature curve networks from unstructured point
cloud data. Our framework first generates a set of initial curved segments fitting highly curved regions. We then optimize these curved
segments to respect both data fitting and structural regularities. Finally, the optimized curved segments are extended and connected
into curve networks using a clustering method. To facilitate effectiveness in case of severe missing data and to resolve ambiguities, we
develop a user interface for completing the curve networks. Experiments on various imperfect point cloud data validate the effectiveness
of our curve network extraction framework. We demonstrate the usefulness of the extracted curve networks for surface reconstruction
from incomplete point clouds.
Curve Network, Surface Reconstruction, Feature Curve, Point Cloud, Regularity
@inproceedings{yuanhao2016curve,
title={Curve Networks for Surface Reconstruction},
author={Yuanhao Cao, Liangliang Nan, and Peter Wonka},
url={http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20160829142619397233541},
year={2016},
}
Yuanhao Cao, Liangliang Nan, and Peter Wonka. Curve Networks for Surface Reconstruction. 2016. http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20160829142619397233541.