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NeX360: Real-Time All-Around View Synthesis With Neural Basis Expansion.

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    NeX enhances multiplane images (MPI) for real-time novel view synthesis, accurately reproducing view-dependent effects. This new approach also enables real-time rendering of unbounded 360° scenes.

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    Area of Science:

    • Computer Vision
    • Computer Graphics
    • Neural Rendering

    Background:

    • Novel view synthesis aims to generate new images of a scene from unseen viewpoints.
    • Traditional multiplane images (MPI) struggle with complex view-dependent effects and fine details.
    • Real-time rendering of complex scenes with view-dependent effects remains a significant challenge.

    Purpose of the Study:

    • To introduce NeX, a novel approach for real-time novel view synthesis.
    • To improve the modeling of view-dependent effects and fine details in MPI.
    • To extend NeX for rendering unbounded 360° scenes using knowledge distillation.

    Main Methods:

    • NeX parameterizes pixels using neural network-learned spherical basis functions for view-dependent effects.
    • A hybrid implicit-explicit modeling strategy enhances fine detail reproduction.
    • Knowledge distillation is employed to train multiple MPIs for unbounded scene rendering.

    Main Results:

    • NeX successfully reproduces complex view-dependent effects, including challenging cases like rainbow reflections.
    • The method achieves real-time rendering performance.
    • Evaluations on benchmark datasets (NeRF-Synthetic, Light Field, Real Forward-Facing, Space, Shiny) demonstrate superior results.

    Conclusions:

    • NeX offers a significant advancement in real-time novel view synthesis and rendering of complex scenes.
    • The proposed technique effectively models view-dependent effects and fine details.
    • NeX provides a robust solution for both bounded and unbounded scene rendering, outperforming existing real-time methods.