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Related Concept Videos

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Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.
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Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
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Related Experiment Video

Updated: Sep 9, 2025

Photorealistic Learned Landscapes for Augmented Reality
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Virtual Staging of Indoor Panoramic Images via Multi-task Learning and Inverse Rendering.

Uzair Shah, Sara Jashari, Muhammad Tukur

    IEEE Computer Graphics and Applications
    |September 3, 2025
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    Summary
    This summary is machine-generated.

    Virtual staging of indoor scenes is now interactive with VISPI (Virtual Staging Pipeline for Single Indoor Panoramic Images). This framework uses deep learning and real-time rendering for realistic virtual staging from single 360° images.

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    Area of Science:

    • Computer Vision
    • Computer Graphics
    • Artificial Intelligence

    Background:

    • 360° indoor imagery offers cost-effective immersive content creation.
    • Virtual staging, including object removal and realistic insertion with lighting, presents significant technical challenges.

    Purpose of the Study:

    • To introduce VISPI (Virtual Staging Pipeline for Single Indoor Panoramic Images), a novel framework for interactive virtual staging.
    • To enable the manipulation of indoor scenes from single panoramic images.

    Main Methods:

    • A multi-task deep learning approach utilizing a vision transformer to extract geometric, semantic, and material properties.
    • Integration of spherical Gaussian lighting estimation and real-time rendering for interactive object placement.
    • Development of a dual-pathway system for clutter-free scene representation and material property estimation (metallic, roughness).

    Main Results:

    • Simultaneous prediction of depth, normals, semantics, albedo, and material properties from cluttered indoor scenes.
    • Successful generation of stereoscopic Multi-Center-Of-Projection views for Head Mounted Display exploration.
    • Demonstrated effectiveness of the framework on Structured3D and FutureHouse datasets.

    Conclusions:

    • VISPI provides an effective solution for interactive virtual staging using single panoramic images.
    • The framework facilitates realistic virtual environment creation and editing.
    • Applications include enhanced real estate visualization, interior design, and immersive virtual experiences.