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 authorSame journal

Semantic Composition via Optimal Transport for Composed Image Retrieval.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same author

A retrospective study of the relationship between low-density lipoprotein cholesterol and pregnancy outcomes after assisted reproductive technology treatments in women with diabetes mellitus and infertility.

AJOG global reports·2026
Same author

Morphological feature remodeling of intracranial arteries in the context of inflammation and HIV-associated cognitive impairment.

medRxiv : the preprint server for health sciences·2026
Same author

Comparison of DANTE Blood-Suppressed and Conventional SPACE for Post-contrast 3D T1-weighted Intracranial Vessel Wall MRI in Moyamoya Vasculopathy.

AJNR. American journal of neuroradiology·2026
Same author

SNAP MRI reveals association between distal cerebral arterial flow and cognitive function in an aging population.

Magnetic resonance imaging·2026
Same author

Risk factors for co-existing extracranial carotid and intracranial artery high-risk atherosclerotic plaques in middle-aged and elderly patients: a Chinese atherosclerosis risk evaluation (CARE-II) study.

Acta radiologica (Stockholm, Sweden : 1987)·2026

Related Experiment Video

Updated: Mar 3, 2026

Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging
09:19

Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging

Published on: April 18, 2025

1.6K

A Systematic Approach for Cross-Source Point Cloud Registration by Preserving Macro and Micro Structures.

Xiaoshui Huang, Jian Zhang, Lixin Fan

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |April 25, 2017
    PubMed
    Summary

    This study introduces a novel method for registering cross-source point clouds, overcoming challenges like missing data and noise. The approach uses macro and micro structures for robust point cloud registration, outperforming existing algorithms.

    More Related Videos

    Correlative Microscopy for 3D Structural Analysis of Dynamic Interactions
    13:43

    Correlative Microscopy for 3D Structural Analysis of Dynamic Interactions

    Published on: June 24, 2013

    14.6K
    Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities
    07:13

    Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

    Published on: October 27, 2023

    1.7K

    Related Experiment Videos

    Last Updated: Mar 3, 2026

    Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging
    09:19

    Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging

    Published on: April 18, 2025

    1.6K
    Correlative Microscopy for 3D Structural Analysis of Dynamic Interactions
    13:43

    Correlative Microscopy for 3D Structural Analysis of Dynamic Interactions

    Published on: June 24, 2013

    14.6K
    Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities
    07:13

    Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

    Published on: October 27, 2023

    1.7K

    Area of Science:

    • Computer Vision
    • Geomatics Engineering
    • Robotics

    Background:

    • Point cloud registration is crucial for 3D reconstruction and analysis.
    • Cross-source point cloud registration faces challenges like missing data, varying density, scale differences, noise, and outliers.
    • Existing methods struggle with the complexities of diverse sensor data.

    Purpose of the Study:

    • To develop a robust and systematic approach for registering point clouds from different sensors (cross-source).
    • To address the inherent challenges of missing data, noise, and scale variations in cross-source point cloud registration.
    • To improve the accuracy and reliability of point cloud registration for diverse applications.

    Main Methods:

    • Extraction of macro (overall geometric layout) and micro (local segments) structures from point clouds.
    • Representation of structures using graphs and conversion of registration to graph matching.
    • Development of a novel descriptor for discriminative feature space graph matching.
    • Integration of graph matching with RANSAC (Random Sample Consensus) and ICP (Iterative Closest Point) for refinement.

    Main Results:

    • The proposed method demonstrates robust performance in cross-source point cloud registration, even with significant data imperfections.
    • Outperforms eight state-of-the-art registration algorithms on challenging datasets like the Pisa Cathedral.
    • Achieves significantly better performance on over 27 cross-source cases and shows high accuracy on same-source datasets.

    Conclusions:

    • The proposed graph-matching-based approach effectively handles the complexities of cross-source point cloud registration.
    • The method's robustness stems from its ability to leverage both macro and micro structural information.
    • This systematic approach offers a significant advancement in point cloud registration accuracy and reliability.