- The paper introduces a high-fidelity NeRF editing approach that decomposes scenes into low- and high-frequency components for improved multiview consistency.
- It leverages low-frequency adjustments to enable stable style transfers across scenes, significantly enhancing LPIPS and RMSE metrics.
- The framework offers real-time intensity control for fine-tuned, detailed 3D edits, making it ideal for scalable AR/VR applications.
High-Fidelity and Transferable NeRF Editing by Frequency Decomposition
Overview
This paper presents an innovative methodology for high-fidelity and transferable Neural Radiance Fields (NeRF) editing by leveraging frequency decomposition. Traditional NeRF editing techniques often suffer from blurriness and a lack of detailed structures, primarily due to inconsistencies introduced during 2D to 3D liftings. The authors propose a novel approach to circumvent these issues by editing the lower frequency components of the image, as these components are naturally more multiview-consistent post-editing. This paper sheds light on the implications that the low-frequency domain is crucial for coherent multiview representations, while the high-frequency components encapsulate scene-specific details.
Methodology
The core of this research lies in decomposing the NeRF into low-frequency and high-frequency components. The authors posit that appearance styles are prominently dictated by low-frequency signals, while fine content details are in the high-frequency domain. This demarcation allows for stylistic edits to occur within the low-frequency feature space, providing benefits of stable intensity control and the seamless transfer of styles across different scenes without retraining. The proposed method builds a high-fidelity edited NeRF scene by blending the high-frequency details from the original scene with the edited low-frequency components.
Evaluation and Results
Per-Scene Editing
The methodology's superiority is demonstrated through comprehensive experiments across multiple datasets. Quantitative evaluations illustrate that the proposed method excels in multiview consistency when compared against existing methods like Instruct-NeRF2NeRF. Specifically, the paper reports LPIPS and RMSE metrics for short and long-term consistency, showing marked improvement:
- Short-Term Consistency: LPIPS improves from 0.104 to 0.092, and RMSE decreases from 0.023 to 0.019.
- Long-Term Consistency: LPIPS improves from 0.414 to 0.388, and RMSE decreases from 0.061 to 0.062.
Additionally, the research highlights the quality and sharpness of the rendered images. Using BRISQUE and sharpness scores, the results show that their method maintains high-fidelity details comparable to the original scene, unlike the comparative methods where significant blurriness is noted.
Transferability and Intensity Control
The proposed framework's distinctive advantage is its transferability. The stylization module, once trained on a given scene, can be effortlessly transferred to novel scenes, saving considerable computational resources. This is substantiated with qualitative results showcasing high-fidelity edits across different scenes with consistent quality and detail.
Moreover, the framework facilitates flexible intensity control, allowing the stylization level to be adjusted in real-time during inference. This is accomplished by interpolating between the original and edited low-frequency features, enabling nuanced transitions and fine-grained adjustments, a feature absent in conventional methods.
Implications and Future Work
The implications of this research are manifold. Practically, it enhances the efficiency and scalability of NeRF editing frameworks, enabling wide-ranging applications in AR/VR environments, virtual simulations, and beyond. Theoretically, it underscores the significance of frequency domain transformations in achieving multiview consistency and high fidelity, prompting further exploration into more sophisticated editing techniques leveraging similar principles.
The method, while effective, does present limitations. Notably, in scenarios with significant geometric alterations, the blending process may introduce artifacts, necessitating intelligent alpha-blending techniques. Future research directions could focus on adaptive blending strategies and further optimization of the feature-space manipulation to minimize any residual inconsistencies and enhance the overall robustness of the editing framework.
Conclusion
This paper delineates a robust NeRF editing strategy by incorporating frequency decomposition, substantially improving both the fidelity and transferability of 3D scene edits. The clear distinction between low and high-frequency component manipulations facilitates more consistent multiview representations and controlled stylistic interventions, paving the way for more scalable and high-quality NeRF-based applications. The promising results and significant computational benefits underscore the potential for adopting similar techniques in broader AI-driven rendering and editing workflows.