PLReg3D: Learning 3D Local and Global Descriptors Jointly for Global Localization-Reference-Cited by-同舟云学术

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PLReg3D: Learning 3D Local and Global Descriptors Jointly for Global Localization

Published:2021-11-26 Issue: Volume: Page:
ISSN:
Container-title:2021 27th International Conference on Mechatronics and Machine Vision in Practice (M2VIP)
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Author:

Qiao Zhijian,Wang Hanwen,Zhu Yu,Wang Hesheng

Funder

Shanghai Education Development Foundation

Science and Technology Commission of Shanghai Municipality

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9664973/9664984/09665098.pdf?arnumber=9665098

Reference25 articles.

1. LPD-Net: 3D Point Cloud Learning for Large-Scale Place Recognition and Environment Analysis

2. MinkLoc3D: Point Cloud Based Large-Scale Place Recognition

3. End-to-End 3D Point Cloud Learning for Registration Task Using Virtual Correspondences

4. PointNetLK: Robust & Efficient Point Cloud Registration Using PointNet

5. Predator: Registration of 3d point clouds with low overlap;huang,2020

Cited by 1 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. Loop Closure Detection Based on Local and Global Descriptors with Sinkhorn Algorithm;Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering;2024

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