PMHA-Net: Positional Multi-Head Attention Network for Point-Cloud Part Segmentation and Classification-Reference-Cited by-同舟云学术

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PMHA-Net: Positional Multi-Head Attention Network for Point-Cloud Part Segmentation and Classification

Published:2023 Issue: Volume:11 Page:117920-117934
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Jeon Jaeseung¹^ORCID,Hong Seokjin¹^ORCID,Lee Hookyung¹^ORCID,Kim Jeesu²^ORCID,Yoo Jinwoo³^ORCID

Affiliation:

1. Graduate School of Automotive Engineering, Kookmin University, Seoul, Republic of Korea

2. Departments of Cogno-Mechatronics Engineering and Optics & Mechatronics Engineering, Pusan National University, Busan, Republic of Korea

3. Department of Automobile and IT Convergence, Kookmin University, Seoul, Republic of Korea

Funder

National Research Foundation of Korea (NRF) grants funded by the Korean government

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10274967.pdf?arnumber=10274967

Reference50 articles.

1. 3DMV: Joint 3D-Multi-view Prediction for 3D Semantic Scene Segmentation

2. Learning Local Shape Descriptors from Part Correspondences with Multiview Convolutional Networks

3. Dynamic Graph CNN for Learning on Point Clouds

4. An Improved Multi-View Convolutional Neural Network for 3D Object Retrieval

5. Multi-view Convolutional Neural Networks for 3D Shape Recognition

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