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

Structural Classification of Joints01:20

Structural Classification of Joints

4.7K
Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
A fibrous joint is where the adjacent bones are united by fibrous connective...
4.7K
Design Example: Measuring Distance Between Two Points with Obstructions01:10

Design Example: Measuring Distance Between Two Points with Obstructions

151
When measuring distances in areas with physical obstructions, such as a lake in a field, surveyors must employ techniques to calculate accurate lengths without direct line measurements. One effective method is the offset technique, which allows for precise distance estimation over inaccessible stretches.In this scenario, a surveyor must measure a side of an area that crosses a lake. Since the measuring tape cannot span the lake, the surveyor begins by establishing a baseline that aligns with...
151

You might also read

Related Articles

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

Sort by
Same author

Relation DETR+: Exploring Explicit Position Relation Prior for Dense Prediction.

IEEE transactions on pattern analysis and machine intelligence·2026
Same author

Brain network construction and analysis for epilepsy: A methodology review.

Neural networks : the official journal of the International Neural Network Society·2026
Same author

Real-time robust autofocus method enabling sustained intravital scanning light field imaging.

Nature communications·2026
Same author

A multi-modal foundation model for brain disease diagnosis and medical imaging.

Patterns (New York, N.Y.)·2026
Same author

Robust point cloud registration based on semantic iterative closest point algorithm.

Fundamental research·2026
Same author

Modulation of place cells using targeted stimulation with bidirectional microelectrode arrays enhances spatial learning speed in mice.

Fundamental research·2026

Related Experiment Video

Updated: Oct 4, 2025

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

1.4K

STORM: Structure-Based Overlap Matching for Partial Point Cloud Registration.

Yujie Wang, Chenggang Yan, Yutong Feng

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |February 4, 2022
    PubMed
    Summary

    The new StrucTure-based OveRlap Matching (STORM) method effectively registers partial point clouds, even with small overlaps. STORM overcomes limitations of traditional methods by accurately identifying correspondences using structural information.

    More Related Videos

    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

    876
    Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization
    05:49

    Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization

    Published on: February 23, 2024

    1.0K

    Related Experiment Videos

    Last Updated: Oct 4, 2025

    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

    1.4K
    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

    876
    Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization
    05:49

    Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization

    Published on: February 23, 2024

    1.0K

    Area of Science:

    • Computer Vision
    • 3D Geometry Processing
    • Computational Geometry

    Background:

    • Partial point cloud registration is crucial for reconstructing complete 3D shapes from incomplete data.
    • Traditional methods like Iterative Closest Point (ICP) struggle with small or insufficient overlaps between point clouds.
    • Existing methods often fail due to their inability to distinguish outlier correspondences in low-overlap scenarios.

    Purpose of the Study:

    • To introduce a novel method, StrucTure-based OveRlap Matching (STORM), for robust partial point cloud registration.
    • To address the limitations of existing registration techniques in handling small overlap ratios.
    • To improve the accuracy and reliability of 3D shape reconstruction from partial scans.

    Main Methods:

    • Developed an overlap prediction module utilizing differentiable sampling to detect points within overlapping regions.
    • Employed graph-based methods to extract pointwise features that encode effective structural information.
    • Generated accurate partial correspondences based on discriminative pointwise feature similarity.

    Main Results:

    • STORM demonstrated superior performance compared to state-of-the-art partial point cloud registration methods.
    • The method achieved satisfactory results even when the overlap ratio between point clouds was significantly reduced.
    • Experimental validation confirmed the effectiveness of the structure-based overlap prediction and correspondence generation.

    Conclusions:

    • STORM offers a significant advancement in partial point cloud registration, particularly for challenging low-overlap cases.
    • The proposed approach enhances the generation of complete 3D shapes by improving the accuracy of partial scan alignment.
    • This method provides a more reliable solution for 3D reconstruction tasks where complete data is not initially available.