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

Estimation of the Physical Quantities01:05

Estimation of the Physical Quantities

8.6K
On many occasions, physicists, other scientists, and engineers need to make estimates of a particular quantity. These are sometimes referred to as guesstimates, order-of-magnitude approximations, back-of-the-envelope calculations, or Fermi calculations. The physicist Enrico Fermi was famous for his ability to estimate various kinds of data with surprising precision. Estimating does not mean guessing a number or a formula at random. Instead, estimation means using prior experience and sound...
8.6K

You might also read

Related Articles

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

Sort by
Same author

Social marginalization risk and its negative association with socialising preferences in Japanese gamers.

PloS one·2026
Same author

A learning curve analysis of resident-performed laparoscopic transabdominal preperitoneal inguinal hernia repair and the impact of the operative interval.

Surgery today·2026
Same author

Optimizing COPD Care in Belgium: A Multidisciplinary Expert Consensus on Cardiopulmonary Risk Management.

International journal of chronic obstructive pulmonary disease·2026
Same author

Tag-Along Virtual Windows Increase Perceived Resistance and Task Load in Augmented Reality.

IEEE transactions on visualization and computer graphics·2026
Same author

Electrooculography-Based Detection of Refractive Vision Problems.

IEEE journal of biomedical and health informatics·2026
Same author

Visual and Somatosensory Integration With Higher Sitting Posture Enhances the Sense of Standing and Self-Motion in Seated VR.

IEEE transactions on visualization and computer graphics·2025

Related Experiment Video

Updated: Mar 27, 2026

Photorealistic Learned Landscapes for Augmented Reality
06:54

Photorealistic Learned Landscapes for Augmented Reality

Published on: June 27, 2025

859

Pose Estimation for Augmented Reality: A Hands-On Survey.

Eric Marchand, Hideaki Uchiyama, Fabien Spindler

    IEEE Transactions on Visualization and Computer Graphics
    |January 6, 2016
    PubMed
    Summary

    This study introduces vision-based camera localization methods for augmented reality (AR). It surveys key approaches and extensions, providing code examples to bridge theory and practice for accurate virtual object integration.

    More Related Videos

    Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping
    09:41

    Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

    Published on: April 21, 2023

    2.4K
    Pedicle Screw Placement Using an Augmented Reality Head-Mounted Display in a Porcine Model
    06:18

    Pedicle Screw Placement Using an Augmented Reality Head-Mounted Display in a Porcine Model

    Published on: May 24, 2024

    2.9K

    Related Experiment Videos

    Last Updated: Mar 27, 2026

    Photorealistic Learned Landscapes for Augmented Reality
    06:54

    Photorealistic Learned Landscapes for Augmented Reality

    Published on: June 27, 2025

    859
    Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping
    09:41

    Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

    Published on: April 21, 2023

    2.4K
    Pedicle Screw Placement Using an Augmented Reality Head-Mounted Display in a Porcine Model
    06:18

    Pedicle Screw Placement Using an Augmented Reality Head-Mounted Display in a Porcine Model

    Published on: May 24, 2024

    2.9K

    Area of Science:

    • Computer Vision
    • Computer Graphics
    • Robotics

    Background:

    • Augmented reality (AR) requires seamless integration of virtual objects into real-world image sequences.
    • Accurate rendering and alignment of synthetic elements are crucial for visually acceptable AR experiences.
    • Camera localization, or pose estimation, is fundamental to achieving this precise integration.

    Purpose of the Study:

    • To provide a self-contained introduction to vision-based camera localization techniques.
    • To survey recent extensions and advancements in the field.
    • To facilitate the transition from theoretical understanding to practical implementation.

    Main Methods:

    • Review of established vision-based camera localization algorithms.
    • Survey of recent extensions and novel approaches.
    • Inclusion of code examples for practical application.

    Main Results:

    • A comprehensive overview of significant camera localization methods.
    • Identification of key extensions enhancing localization accuracy and robustness.
    • Accessible code snippets for demonstrating theoretical concepts.

    Conclusions:

    • Vision-based camera localization is essential for realistic augmented reality.
    • The presented survey offers a valuable resource for researchers and developers.
    • Practical code examples lower the barrier to entry for implementing advanced localization techniques.