NeRF-Insert: Local 3D Editing with Multimodal Control Signals
The paper "NeRF-Insert: Local 3D Editing with Multimodal Control Signals" introduces a framework for editing Neural Radiance Fields (NeRFs) that emphasizes locality and control granularity, significantly advancing the utility and flexibility of 3D scene management. The authors propose a novel approach that redefines scene editing as an inpainting problem, leveraging multiple modalities for control and reference. This technique stands in contrast with existing methods that predominantly depend on text-based conditioning for global edits.
Core Contributions and Methodology
The central contribution of the paper is the development of NeRF-Insert, which demonstrates an effective method of integrating local edits into NeRF frameworks. This system utilizes various inputs, including textual prompts, reference images, CAD models, and manually-drawn image masks, allowing users to specify and modify 3D regions explicitly. The core technique involves transforming user-specified region selections, which can be defined by a minimal number of manually-drawn masks or precise CAD models, into a 3D visual hull. This hull guides the inpainting process across viewpoints while respecting the scene's global structure.
The NeRF-Insert framework employs an Iterative Dataset Update (IDU) protocol to distill 2D edits into the 3D space. This mechanism replaces existing models that may indiscriminately alter the scene structure with updates that favor maintaining original consistency within non-edited areas. By using generic image generation models and rendering from multiple viewpoints, the system lifts these edits to a 3D-consistent NeRF model, achieving higher fidelity in visual output compared to previous models.
Key Findings and Implications
Empirically, NeRF-Insert showcases higher visual quality and retains stronger consistency with the original NeRF scene structures than previous methods like Instruct-NeRF2NeRF. The versatility of the multimodal inputs provides users with a spectrum of control levels, from loosely defining an object with textual prompts to precisely positioning it using mesh models. Notably, the research introduces a loss term that enforces spatial constraints, diminishing undesired alterations outside the targeted edit region. This directly contributes to decreased artifacts such as floaters and improves overall edit quality.
From a theoretical perspective, this work pioneers avenues in 3D scene editing by introducing multimodal inputs to conditional inpainting processes. Practically, the framework offers a toolkit potentially incorporable with other 3D models or emerging diffusion models, enhancing their ability to handle more complex scenarios and larger scenes than current single-object datasets.
Future Directions
The authors suggest that their modular approach can integrate newer inpainting models and varied control signals, which could further bolster the effectiveness of 3D scene editing applications. Subsequent research might focus on refining the robustness of spatial constraint enforcement, exploring extended application in dynamic scenes, and further reducing computational load during the IDU process.
NeRF-Insert marks an important evolution in NeRF editing capabilities. By allowing flexibly controlled, high-quality edits, it sets a foundation for extensive future work in the field, particularly concerning interactive 3D content creation and modification. The implications of this development resonate across virtual reality, gaming, and 3D visualization domains, where precise and intuitive scene management is paramount.