BoxNeRF: simplified object selection and editing in NeRF scenes
yingsheng shao, Yingwei Yang, haiguang huang · 2025
Over the past few years, NeRF research has led to widespread applications across diverse fields, including virtual filmmaking, product design, and more. However, conveniently editing NeRF scenes still faces challenges. There's a pressing requirement to investigate the editability of NeRF to bolster user interaction with NeRF scenes, facilitating swift modifications to scene appearance and content. At present, a multitude of investigations have focused on NeRF editing, with some constrained to altering overall scenes, while others are confined to particular editing tasks, lacking adaptability and flexibility. We introduce a method called BoxNeRF, which enables efficient and flexible editing operations on objects within NeRF scenes including copy-paste, deletion, and affine transformations, by selecting them within a single 2D image. We use generated 3D masks to perform editing operations on objects and utilize the parameters of the original NeRF model to infer object content instead of optimizing a new NeRF model, thereby preserving the texture details while also maintaining accuracy in both color and geometry. Our approach empowers users to conduct superior scene editing with ease and simplicity while remaining scene coherence. Besides traditional explicit 3D editing operations, we also enable object transfer between scenes and recoloring, resulting in satisfactory editing outcomes. Our method showcases straightforwardness, effectiveness, and high quality, achieving editing speeds in seconds even on a commercial mid-range GPU.