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    Area of Science:

    • Computer Vision
    • Computer Graphics
    • 3D Geometry Processing

    Background:

    • Neural Radiance Fields (NeRFs) excel at novel view synthesis for static 3D scenes.
    • Modifying NeRF content is challenging due to large training datasets.
    • Existing NeRF editing methods lack direct shape deformation capabilities.

    Purpose of the Study:

    • To develop a method for direct geometry editing of Neural Radiance Fields.
    • To enable intuitive shape manipulation of NeRF representations.
    • To extend NeRF editing with advanced features like semantic part manipulation.

    Main Methods:

    • Extract a triangle mesh representation from the NeRF's geometry.
    • Deform the extracted mesh using tools like ARAP deformation.
    • Map deformations back to the NeRF volume for modified rendering.
    • Introduce box abstractions and semantic labels for enhanced editing control.

    Main Results:

    • Successfully implemented direct shape deformation for NeRFs.
    • Demonstrated intuitive editing through box abstractions and semantic part combination.
    • Validated the method on both synthetic and real-world captured scenes.

    Conclusions:

    • The proposed method significantly enhances the editability of NeRFs, particularly for shape manipulation.
    • It bridges the gap between implicit NeRF representations and explicit geometry editing.
    • Offers a powerful new tool for interactive 3D scene modification.