Unsupervised Deep Probabilistic Approach for Partial Point Cloud Registration-Reference-Cited by-同舟云学术

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Unsupervised Deep Probabilistic Approach for Partial Point Cloud Registration

Published:2023-06 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
language:
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Author:

Mei Guofeng¹,Tang Hao²,Huang Xiaoshui³,Wang Weijie⁴,Liu Juan⁵,Zhang Jian¹,Van Gool Luc²,Wu Qiang¹

Affiliation:

1. GBDTC, UTS

2. CVL, ETH Zurich

3. Shanghai AI Lab

4. UNITN

5. BIT

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10203037/10203050/10204460.pdf?arnumber=10204460

Reference57 articles.

1. HGMR: Hierarchical Gaussian Mixtures for Adaptive 3D Registration

2. Loop Closure Detection Using Local 3D Deep Descriptors

3. PPF-FoldNet: Unsupervised Learning of Rotation Invariant 3D Local Descriptors

4. Fast global registration;zhou;ECCV,0

5. Joint Alignment of Multiple Point Sets with Batch and Incremental Expectation-Maximization

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1. RCFI-Net: A reliable correspondences evaluation and feature interaction network for fast and accurate point cloud registration;Applied Soft Computing;2024-09

2. Enhancing point cloud registration with transformer: cultural heritage protection of the Terracotta Warriors;Heritage Science;2024-08-29

3. Advances in 3D pre-training and downstream tasks: a survey;Vicinagearth;2024-07-23

4. DBDNet:Partial-to-partial point cloud registration with dual branches decoupling;Knowledge-Based Systems;2024-07

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

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