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Related Experiment Video

Updated: Jul 9, 2025

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters
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NeRF-In: Free-Form Inpainting for Pretrained NeRF With RGB-D Priors.

I-Chao Shen, Hao-Kang Liu, Bing-Yu Chen

    IEEE Computer Graphics and Applications
    |November 28, 2023
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel framework for intuitive editing of Neural Radiance Fields (NeRF). It allows users to retouch unwanted regions in pretrained NeRF scenes without needing training data.

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    Area of Science:

    • Computer Vision
    • Computer Graphics
    • Machine Learning

    Background:

    • Neural Radiance Fields (NeRF) offer powerful scene representation capabilities.
    • Editing pretrained NeRF models is challenging due to the implicit association between network parameters and scene appearance.

    Purpose of the Study:

    • To develop a user-friendly framework for retouching specific regions within pretrained NeRF scenes.
    • To enable intuitive scene editing without requiring original training data or category-specific priors.

    Main Methods:

    • Users draw free-form masks on rendered views to identify unwanted regions.
    • The framework transfers masks across views and estimates guiding color and depth information.
    • An optimization process updates NeRF parameters to inpaint masked regions.

    Main Results:

    • The framework successfully retouches undesired objects in diverse NeRF scenes.
    • Results demonstrate visually plausible and structurally consistent scene edits.
    • The method requires minimal user effort for effective scene manipulation.

    Conclusions:

    • This work presents the first framework for intuitive, mask-based editing of pretrained NeRF models.
    • The approach facilitates easier modification of complex 3D scenes represented by NeRF.
    • The proposed method offers a practical solution for users to refine NeRF-based scene representations.