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

Stereotype Content Model02:16

Stereotype Content Model

14.0K
The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
14.0K

You might also read

Related Articles

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

Sort by
Same author

EnergiQ: A Prescriptive Large Language Model-Driven Intelligent Platform for Interpreting Appliance Energy Consumption Patterns.

Sensors (Basel, Switzerland)·2025
Same author

Hierarchical Resources Management System for Internet of Things-Enabled Smart Cities.

Sensors (Basel, Switzerland)·2025
Same author

Sensors and Advanced Sensing Techniques for Computer Vision Applications.

Sensors (Basel, Switzerland)·2025
See all related articles

Related Experiment Video

Updated: Jun 17, 2025

Creating Virtual-hand and Virtual-face Illusions to Investigate Self-representation
06:53

Creating Virtual-hand and Virtual-face Illusions to Investigate Self-representation

Published on: March 1, 2017

13.2K

ARM4CH: A Methodology for Autonomous Reality Modelling for Cultural Heritage.

Nikolaos Giakoumidis1,2, Christos-Nikolaos Anagnostopoulos2

  • 1KINESIS Lab, Core Technology Platforms, New York University Abu Dhabi, Abu Dhabi P.O. Box 129188, United Arab Emirates.

Sensors (Basel, Switzerland)
|August 10, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces autonomous robotic agents for 3D Reality Modeling (RM) of Cultural Heritage (CH) monuments, reducing manual labor. This Industry 4.0 approach ensures systematic, accurate, and repeatable digitization for CH management and digital twins.

Keywords:
LiDARNext Best ViewUAVautonomous robotsreality modelingterrestrial laser scanning

More Related Videos

Author Spotlight: Revolutionizing Remote Surgery with Augmented Reality and Robotics for Enhanced Precision and Accessibility
07:46

Author Spotlight: Revolutionizing Remote Surgery with Augmented Reality and Robotics for Enhanced Precision and Accessibility

Published on: August 9, 2024

681
A Field Primer for Monitoring Benthic Ecosystems Using Structure-From-Motion Photogrammetry
06:36

A Field Primer for Monitoring Benthic Ecosystems Using Structure-From-Motion Photogrammetry

Published on: April 15, 2021

3.7K

Related Experiment Videos

Last Updated: Jun 17, 2025

Creating Virtual-hand and Virtual-face Illusions to Investigate Self-representation
06:53

Creating Virtual-hand and Virtual-face Illusions to Investigate Self-representation

Published on: March 1, 2017

13.2K
Author Spotlight: Revolutionizing Remote Surgery with Augmented Reality and Robotics for Enhanced Precision and Accessibility
07:46

Author Spotlight: Revolutionizing Remote Surgery with Augmented Reality and Robotics for Enhanced Precision and Accessibility

Published on: August 9, 2024

681
A Field Primer for Monitoring Benthic Ecosystems Using Structure-From-Motion Photogrammetry
06:36

A Field Primer for Monitoring Benthic Ecosystems Using Structure-From-Motion Photogrammetry

Published on: April 15, 2021

3.7K

Area of Science:

  • Robotics and Automation
  • Digital Heritage
  • Surveying and Geomatics

Background:

  • Current 3D Reality Modeling (RM) of Cultural Heritage (CH) relies on manual 3D scanning and photogrammetry.
  • This traditional process requires significant expert intervention, involving laborious planning and time-consuming execution.
  • Site-specific constraints and requirements add complexity to manual 3D digitization efforts.

Purpose of the Study:

  • To propose a novel methodology for autonomous 3D Reality Modeling (RM) of Cultural Heritage (CH) monuments.
  • To minimize human intervention in the digitization process through automation.
  • To introduce an Industry 4.0-based approach for reality modeling and surveying of cultural spaces.

Main Methods:

  • Employing autonomous robotic agents equipped with advanced sensors (e.g., terrestrial, mobile 3D scanners, photogrammetric imaging).
  • Developing a systematic and repeatable methodology for autonomous 3D data acquisition.
  • Implementing an Industry 4.0 framework for automated reality modeling.

Main Results:

  • Autonomous robotic agents can perform 3D RM systematically, repeatably, and accurately.
  • Reduced need for manual labor and expert intervention in CH digitization.
  • The methodology lays the groundwork for future real-life scenario evaluations.

Conclusions:

  • Autonomous robotic agents offer a viable solution for efficient and accurate 3D Reality Modeling of Cultural Heritage.
  • The proposed Industry 4.0 methodology enhances the digitization of cultural spaces.
  • Automated RM has potential applications in digital twin platforms for CH monitoring and management.