UAVEdit-NeRFDiff: Controllable Region Editing for Large-Scale UAV Scenes Using Neural Radiance Fields and Diffusion Models

The integration of Neural Radiance Field (NeRF)-based 3D reconstruction with text-guided diffusion models enables flexible editing of real-world scenes. However, for large-scale UAV-captured scenes, existing methods struggle to achieve strong semantic consistency (e.g., in local editing) and suffer from cross-view inconsistency, primarily due to the globally free generative behavior and the lack of scene continuity constraints in diffusion models. To address these issues, we propose the UAVEdit-NeRFDiff framework, which ensures the maintenance of overall symmetry by restricting the editing operations to the target region. First, we leverage both visual priors and semantic masks to achieve semantically consistent editing for key views, and then design Optimal Editing Propagation (OEP) and Progressive Inheritance Propagation (PIP) methods to achieve cross-view geometric consistency propagation for Single-View-Dependent Regions (SVDRs) and Multi-View-Dependent Regions (MVDRs). Finally, experiments on diverse editing tasks demonstrate our method’s superiority in semantic alignment, cross-view consistency, and visual fidelity on UAV scenes, with promising applications in weather and disaster scenario simulations. On the proposed TDB metric, our approach delivers more than 50% improvement over prior methods. To the best of our knowledge, this is the first text–visual bimodal-guided diffusion editing framework for NeRF-reconstructed UAV-captured scenes, offering a practical and effective route for related research.