EP-Net: Improving Point Cloud Learning Efficiency Through Feature Decoupling-Reference-Cited by-同舟云学术

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EP-Net: Improving Point Cloud Learning Efficiency Through Feature Decoupling

Published:2024 Issue: Volume:73 Page:1-14
ISSN:0018-9456
Container-title:IEEE Transactions on Instrumentation and Measurement
language:
Short-container-title:IEEE Trans. Instrum. Meas.

Author:

Deng Hao¹^ORCID,Chen Shengmei¹^ORCID,Zhu Xuan¹^ORCID,Jiang Bo¹^ORCID,Jing Kunlei²^ORCID,Wang Lin¹^ORCID

Affiliation:

1. State-Province Joint Engineering and Research Center of Advanced Networking and Intelligent Information Services, School of Information Science and Technology, Northwest University, Xi’an, Shaanxi, China

2. Department of Computing, The Hong Kong Polytechnic University, Hong Kong, SAR, China

Funder

Key Research and Development Program of Shaanxi Province of China

National Natural Science Foundation of China

Northwest University Graduate Innovation Project

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/19/10367905/10658997.pdf?arnumber=10658997

Reference49 articles.

1. 6-D Object Pose Estimation Using Multiscale Point Cloud Transformer

2. A Calibration Algorithm of 3-D Point Cloud Acquisition System Based on KMPE Cost Function

3. Learning Compact Transformation Based on Dual Quaternion for Point Cloud Registration

4. PointCNN: Convolution on X-transformed points;Li

5. Relation-Shape Convolutional Neural Network for Point Cloud Analysis

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