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    SIComp is a novel framework for projector compensation, offering setup-independent geometric and photometric correction. It generalizes to new environments without retraining, overcoming key limitations in current methods.

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    Area of Science:

    • Computer Vision
    • Computer Graphics
    • Image Processing

    Background:

    • Projector compensation corrects distortions on nonplanar surfaces.
    • Existing methods are setup-dependent, requiring retraining for new configurations.
    • Limited datasets and rigid models hinder generalization.

    Purpose of the Study:

    • Introduce SIComp, the first setup-independent projector compensation framework.
    • Enable generalization to unseen projector-camera setups without fine-tuning.
    • Address limitations of existing, setup-specific compensation methods.

    Main Methods:

    • Constructed a large-scale dataset with 277 real-world projector-camera setups.
    • Developed a co-adaptive framework decoupling geometry and photometry.
    • Utilized an optical flow module for online geometric correction and a photometric network for compensation.
    • Integrated intensity-varying surface priors for robustness under varying illumination.

    Main Results:

    • SIComp achieves high-quality compensation across diverse, unseen setups.
    • Demonstrated superior generalization ability compared to existing methods.
    • Established the first generalizable solution for full projector compensation.

    Conclusions:

    • SIComp offers a robust and generalizable solution for projector compensation.
    • The framework overcomes the setup-dependency limitations of prior work.
    • Enables reliable image projection correction in dynamic environments.