ML Anthology

Pyramid U-Network for Skeleton Extraction from Shape Points

Rowel Atienza
CVPRW 2019 pp. 1177-1180
doi:10.1109/CVPRW.2019.00155 /cvprw/2019/atienza2019cvprw-pyramid/

Abstract

The knowledge about the skeleton of a given geometric shape has many practical applications such as shape animation, shape comparison, shape recognition, and estimating structural strength. Skeleton extraction becomes a more challenging problem when the topology is represented in point cloud domain. In this paper, we present the network architecture, PSPU-SkelNet, for TeamPH which ranked 3rd in Point SkelNetOn 2019 challenge. PSPU-SkelNet is a pyramid of three U-Nets that predicts the skeleton from a given shape point cloud. PSPU-SkelNet achieves a Chamfer Distance (CD) of 2.9105 on the final test dataset. The code of PSPU SkelNet is available at https://github.com/roatienza/skelnet.

CVPRW Semantic Scholar

Cite

Text

Atienza. "Pyramid U-Network for Skeleton Extraction from Shape Points." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2019. doi:10.1109/CVPRW.2019.00155

Markdown

[Atienza. "Pyramid U-Network for Skeleton Extraction from Shape Points." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2019.](https://mlanthology.org/cvprw/2019/atienza2019cvprw-pyramid/) doi:10.1109/CVPRW.2019.00155

BibTeX

@inproceedings{atienza2019cvprw-pyramid,
  title     = {{Pyramid U-Network for Skeleton Extraction from Shape Points}},
  author    = {Atienza, Rowel},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year      = {2019},
  pages     = {1177-1180},
  doi       = {10.1109/CVPRW.2019.00155},
  url       = {https://mlanthology.org/cvprw/2019/atienza2019cvprw-pyramid/}
}