NDLI: Point-selection and multi-level-point-feature fusion-based 3D point cloud classification

Content Provider	IET Digital Library
Author	Zhang, Qieshi Cheng, Jun Wang, Shengwen Xu, Chengjun Gao, Xiangyang
Abstract	Recent years, the research on object classification based on three-dimensional (3D) point cloud pays more attention to extract the features from point sets directly. PointNet++ is the latest network structure for 3D classification which has achieved acceptable results. Although it has achieved acceptable results, there are still two problems: (i) The farthest point sampling (FPS) algorithm in PointNet++ ignores the fact that the feature of each point contributes differently to the classification and segmentation tasks. Therefore, FPS cannot guarantee that the selected point sets can correctly represent the main features of the object. (ii) The multi-scale grouping and multi-resolution grouping in PointNet++ do not consider the features between different levels of the same point. In order to solve these problems, the authors propose the point-selection structure which can calculate the importance of each point's feature. In addition, multi-level-point-feature fusion module is proposed to extract the features of the point at all levels and fuse them as a new feature of that point. In this Letter, they make some experiments on ModelNet40 and ScanNet datasets, which achieves better results compared to the state-of-the-art methods.
Starting Page	290
Ending Page	293
Page Count	4
ISSN	00135194
Volume Number	56
e-ISSN	1350911X
Issue Number	Issue 6, Mar (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/el/56/6
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/el.2019.2856
Journal	Electronics Letters
Publisher Date	2020-01-17
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Computer Vision And Image Processing Technique Farthest Point Sampling Algorithm Feature Extraction Graphics Technique Image Classification Image Fusion Image Recognition Image Representation Image Segmentation ModelNet40 Datasets Multilevel-point-feature Fusion Module Multilevel-point-feature Fusion-based 3D Point Cloud Classification Multiresolution Grouping Multiscale Grouping Object Classification Point-selection Structure PointNet++ ScanNet Datasets Segmentation Task Sensor Fusion Solid Modelling Three-dimensional Point Cloud
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Hybrid feature CNN model for point cloud classification and segmentation

PUConv: Upsampling convolutional network for point cloud semantic segmentation

Feature-based detection and classification of moving objects using LiDAR sensor

Object-Based Point Cloud Analysis of Full-Waveform Airborne Laser Scanning Data for Urban Vegetation Classification

Review: Deep Learning on 3D Point Clouds

CGR-Block: Correlated Feature Extractor and Geometric Feature Fusion for Point Cloud Analysis

PFCN: a fully convolutional network for point cloud semantic segmentation

DFT-Net: Deep Feature Transformation Based Network for Object Categorization and Part Segmentation in 3-Dimensional Point Clouds

A Distance Correlation Approach for Optimum Multiscale Selection in 3D Point Cloud Classification

Point-selection and multi-level-point-feature fusion-based 3D point cloud classification

Similar Documents

Hybrid feature CNN model for point cloud classification and segmentation

PUConv: Upsampling convolutional network for point cloud semantic segmentation

Feature-based detection and classification of moving objects using LiDAR sensor

Object-Based Point Cloud Analysis of Full-Waveform Airborne Laser Scanning Data for Urban Vegetation Classification

Review: Deep Learning on 3D Point Clouds

CGR-Block: Correlated Feature Extractor and Geometric Feature Fusion for Point Cloud Analysis

PFCN: a fully convolutional network for point cloud semantic segmentation

DFT-Net: Deep Feature Transformation Based Network for Object Categorization and Part Segmentation in 3-Dimensional Point Clouds

A Distance Correlation Approach for Optimum Multiscale Selection in 3D Point Cloud Classification

Point-selection and multi-level-point-feature fusion-based 3D point cloud classification