RFNet: Recurrent Forward Network for Dense Point Cloud Completion-Reference-Cited by-同舟云学术

RFNet: Recurrent Forward Network for Dense Point Cloud Completion

Published:2021-10 Issue: Volume: Page:
ISSN:
Container-title:2021 IEEE/CVF International Conference on Computer Vision (ICCV)
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Author:

Huang Tianxin¹,Zou Hao¹,Cui Jinhao¹,Yang Xuemeng¹,Wang Mengmeng¹,Zhao Xiangrui¹,Zhang Jiangning¹,Yuan Yi²,Xu Yifan²,Liu Yong¹

Affiliation:

1. Zhejiang University

2. NetEase Fuxi AI Lab

Funder

National Science Foundation

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9709627/9709628/09710048.pdf?arnumber=9710048

Reference33 articles.

1. Detail preserved point cloud completion via separated feature aggregation;zhang,2020

2. PCN: Point Completion Network

3. PU-Net: Point Cloud Upsampling Network

4. Foldingnet: Point cloud auto-encoder via deep grid deformation;yang;Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,2018

5. Multi-Angle Point Cloud-VAE: Unsupervised Feature Learning for 3D Point Clouds From Multiple Angles by Joint Self-Reconstruction and Half-to-Half Prediction

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