BluNF: Blueprint Neural Field

TL;DR

BluNF introduces a user-friendly 2D blueprint system for intuitive scene editing in neural radiance fields, enabling precise 3D manipulations through semantic and depth priors, simplifying complex editing tasks.

Contribution

It presents a novel blueprint neural field approach that leverages implicit neural representations for easy scene editing using semantic and depth priors.

Findings

Enables intuitive scene editing via a 2D blueprint.

Supports complex 3D manipulations like masking and object removal.

Significantly improves scene editing efficiency and accuracy.

Abstract

Neural Radiance Fields (NeRFs) have revolutionized scene novel view synthesis, offering visually realistic, precise, and robust implicit reconstructions. While recent approaches enable NeRF editing, such as object removal, 3D shape modification, or material property manipulation, the manual annotation prior to such edits makes the process tedious. Additionally, traditional 2D interaction tools lack an accurate sense of 3D space, preventing precise manipulation and editing of scenes. In this paper, we introduce a novel approach, called Blueprint Neural Field (BluNF), to address these editing issues. BluNF provides a robust and user-friendly 2D blueprint, enabling intuitive scene editing. By leveraging implicit neural representation, BluNF constructs a blueprint of a scene using prior semantic and depth information. The generated blueprint allows effortless editing and manipulation of NeRF representations. We demonstrate BluNF's editability through an intuitive click-and-change mechanism, enabling 3D manipulations, such as masking, appearance modification, and object removal. Our approach significantly contributes to visual content creation, paving the way for further research in this area.