View-Guided Point Cloud Completion

Xuancheng Zhang, Yutong Feng, Siqi Li, Changqing Zou, Hai Wan, Xibin Zhao, Yandong Guo, Yue Gao

CVPR 2021 pp. 15890-15899

doi:10.1109/CVPR46437.2021.01563 /cvpr/2021/zhang2021cvpr-viewguided/

Abstract

This paper presents a view-guided solution for the task of point cloud completion. Unlike most existing methods directly inferring the missing points using shape priors, we address this task by introducing ViPC (view-guided point cloud completion) that takes the missing crucial global structure information from an extra single-view image. By leveraging a framework which sequentially performs effective cross-modality and cross-level fusions, our method achieves significantly superior results over typical existing solutions on a new large-scale dataset we collect for the view-guided point cloud completion task.

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Cite

Text

Zhang et al. "View-Guided Point Cloud Completion." Conference on Computer Vision and Pattern Recognition, 2021. doi:10.1109/CVPR46437.2021.01563

Markdown

[Zhang et al. "View-Guided Point Cloud Completion." Conference on Computer Vision and Pattern Recognition, 2021.](https://mlanthology.org/cvpr/2021/zhang2021cvpr-viewguided/) doi:10.1109/CVPR46437.2021.01563

BibTeX

@inproceedings{zhang2021cvpr-viewguided,
  title     = {{View-Guided Point Cloud Completion}},
  author    = {Zhang, Xuancheng and Feng, Yutong and Li, Siqi and Zou, Changqing and Wan, Hai and Zhao, Xibin and Guo, Yandong and Gao, Yue},
  booktitle = {Conference on Computer Vision and Pattern Recognition},
  year      = {2021},
  pages     = {15890-15899},
  doi       = {10.1109/CVPR46437.2021.01563},
  url       = {https://mlanthology.org/cvpr/2021/zhang2021cvpr-viewguided/}
}