PCAPN: An Enhanced Feature Extraction Framework for Point Cloud-Reference-Cited by-同舟云学术

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PCAPN: An Enhanced Feature Extraction Framework for Point Cloud

Published:2023 Issue: Volume:11 Page:20786-20794
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Ji Yulin¹^ORCID,Zhong Jiandan¹,Li Yingxiang¹,Fu Junjie¹,Liu Jiawei¹

Affiliation:

1. School of Communication Engineering, Chengdu University of Information Technology, Chengdu, China

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/ielx7/6287639/10005208/09881510.pdf?arnumber=9881510

Reference39 articles.

1. PointNGCNN: Deep convolutional networks on 3D point clouds with neighborhood graph filters

2. A scalable active framework for region annotation in 3D shape collections

3. Gradient-based learning applied to document recognition

4. Very deep convolutional networks for large-scale image recognition;simonyan;arXiv 1409 1556,2014

5. PointConv: Deep Convolutional Networks on 3D Point Clouds

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