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Related Concept Videos

Absolute Motion Analysis- General Plane Motion01:24

Absolute Motion Analysis- General Plane Motion

Visualize a drone, with its propellers spinning rapidly, hovering mid-air. The fascinating movements and operations of this drone can be comprehended by applying the principle of general plane motion.
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Planar Rigid-Body Motion01:22

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Related Experiment Video

Updated: May 29, 2026

Photorealistic Learned Landscapes for Augmented Reality
06:54

Photorealistic Learned Landscapes for Augmented Reality

Published on: June 27, 2025

Handling Motion-Blur in 3D Tracking and Rendering for Augmented Reality.

Youngmin Park, Vincent Lepetit, Woontack Woo

    IEEE Transactions on Visualization and Computer Graphics
    |September 21, 2011
    PubMed
    Summary

    This study enhances the ESM algorithm for 3D object tracking under motion blur, improving accuracy and robustness. It also introduces an efficient rendering method for realistically blurred virtual objects.

    Related Experiment Videos

    Last Updated: May 29, 2026

    Photorealistic Learned Landscapes for Augmented Reality
    06:54

    Photorealistic Learned Landscapes for Augmented Reality

    Published on: June 27, 2025

    Area of Science:

    • Computer Vision
    • Robotics
    • Image Processing

    Background:

    • Template matching algorithms like ESM are crucial for 3D object tracking.
    • Motion blur significantly degrades the performance of existing tracking algorithms.
    • Accurate rendering of blurred virtual objects is computationally expensive.

    Purpose of the Study:

    • To extend the ESM algorithm to effectively handle motion blur in 3D object tracking.
    • To develop an efficient method for rendering virtual objects with motion blur consistent to real images.

    Main Methods:

    • Introduced a generalized ESM algorithm incorporating an image formation model for motion blur.
    • Developed a novel rendering technique using image warping for efficient blurred virtual object generation.

    Main Results:

    • The generalized ESM algorithm demonstrates faster, more accurate, and robust convergence under significant motion blur.
    • The proposed rendering method achieves realistic motion blur effects at a low computational cost.

    Conclusions:

    • The extended ESM algorithm provides a robust solution for 3D object tracking in the presence of motion blur.
    • The efficient rendering technique enables real-time generation of blurred virtual objects, enhancing augmented reality and simulation applications.