CorrNet3D: Unsupervised End-to-end Learning of Dense Correspondence for 3D Point Clouds

Author:

Zeng Yiming,Qian Yue,Zhu Zhiyu,Hou Junhui,Yuan Hui,He Ying

Publisher

IEEE

Cited by 41 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. GTINet: Global Topology-Aware Interactions for Unsupervised Point Cloud Registration;IEEE Transactions on Circuits and Systems for Video Technology;2024-07

2. 3D Shape Correspondence for Medical Applications Using Neural Descriptor Fields;2024 IEEE International Symposium on Biomedical Imaging (ISBI);2024-05-27

3. Dense Correspondence Learning between Key Points in 3D C.elegans Based on Depth Map Matching;2024 3rd International Conference on Image Processing and Media Computing (ICIPMC);2024-05-17

4. Deep learning-based point cloud registration: a comprehensive investigation;International Journal of Remote Sensing;2024-05-08

5. Point cloud registration method for indoor depth sensor acquisition system based on dual graph computation with irregular shape factors;IET Image Processing;2024-04-14

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