Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Dual-Branch Cross-Fusion Normalizing Flow for RGB-D Track Anomaly Detection.

Sensors (Basel, Switzerland)·2025
Same author

An Efficient and Stable Registration Framework for Large Point Clouds at Two Different Moments.

Sensors (Basel, Switzerland)·2024
Same author

Clique-like Point Cloud Registration: A Flexible Sampling Registration Method Based on Clique-like for Low-Overlapping Point Cloud.

Sensors (Basel, Switzerland)·2024
Same author

Improved YOLOv8-Based Target Precision Detection Algorithm for Train Wheel Tread Defects.

Sensors (Basel, Switzerland)·2024
Same author

FR-PatchCore: An Industrial Anomaly Detection Method for Improving Generalization.

Sensors (Basel, Switzerland)·2024
Same author

Polo-like kinase 1 is related with malignant characteristics and inhibits macrophages infiltration in glioma.

Frontiers in immunology·2023
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Oct 5, 2025

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.3K

Matching Algorithm for 3D Point Cloud Recognition and Registration Based on Multi-Statistics Histogram Descriptors.

Jinlong Li1, Bingren Chen1, Meng Yuan1

  • 1School of Physical Science and Technology, Southwest Jiaotong University, Chengdu 610031, China.

Sensors (Basel, Switzerland)
|January 22, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a novel Multi-Statistics Histogram Descriptor (MSHD) and a deep learning key point matching algorithm for 3D point cloud recognition. These methods significantly improve descriptive ability and matching accuracy for robust 3D surface matching.

Keywords:
3D surface matchingfeature descriptorkey point matching algorithmthree-dimensional point cloud

More Related Videos

Four-Dimensional CT Analysis Using Sequential 3D-3D Registration
05:05

Four-Dimensional CT Analysis Using Sequential 3D-3D Registration

Published on: November 23, 2019

8.1K
Reconstruction of 3-Dimensional Histology Volume and its Application to Study Mouse Mammary Glands
10:59

Reconstruction of 3-Dimensional Histology Volume and its Application to Study Mouse Mammary Glands

Published on: July 26, 2014

14.6K

Related Experiment Videos

Last Updated: Oct 5, 2025

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.3K
Four-Dimensional CT Analysis Using Sequential 3D-3D Registration
05:05

Four-Dimensional CT Analysis Using Sequential 3D-3D Registration

Published on: November 23, 2019

8.1K
Reconstruction of 3-Dimensional Histology Volume and its Application to Study Mouse Mammary Glands
10:59

Reconstruction of 3-Dimensional Histology Volume and its Application to Study Mouse Mammary Glands

Published on: July 26, 2014

14.6K

Area of Science:

  • Computer Vision
  • 3D Geometry Processing
  • Machine Learning

Background:

  • Effective local feature descriptors and accurate key point matching are critical for 3D point cloud recognition and registration.
  • Existing methods struggle with noise, occlusion, and incomplete data, impacting descriptor robustness and matching precision.

Purpose of the Study:

  • To propose a novel Multi-Statistics Histogram Descriptor (MSHD) combining spatial and geometric features for enhanced 3D point cloud description.
  • To develop a deep learning-based key point matching algorithm for improved identification of corresponding point pairs.

Main Methods:

  • Developed the Multi-Statistics Histogram Descriptor (MSHD) by integrating spatial distribution and geometric attributes.
  • Implemented a deep learning framework for a new key point matching algorithm.
  • Evaluated performance on the Stanford 3D dataset and real-world component point cloud datasets.

Main Results:

  • MSHD demonstrated superior descriptive ability and robustness to noise and mesh resolution compared to FPFH, SHOT, RoPS, and SpinImage.
  • The deep learning matching algorithm achieved significantly smaller rotation and translation errors.
  • Enhanced recognition and registration accuracy for 3D surface matching was confirmed.

Conclusions:

  • The proposed MSHD and deep learning matching algorithm offer a robust and accurate solution for 3D point cloud recognition and registration.
  • These advancements address limitations of existing methods in handling noisy and incomplete 3D data.
  • The findings pave the way for more reliable 3D surface matching applications.