NeRF-Editing: Geometry Editing of Neural Radiance Fields (2205.04978v1)

Abstract: Implicit neural rendering, especially Neural Radiance Field (NeRF), has shown great potential in novel view synthesis of a scene. However, current NeRF-based methods cannot enable users to perform user-controlled shape deformation in the scene. While existing works have proposed some approaches to modify the radiance field according to the user's constraints, the modification is limited to color editing or object translation and rotation. In this paper, we propose a method that allows users to perform controllable shape deformation on the implicit representation of the scene, and synthesizes the novel view images of the edited scene without re-training the network. Specifically, we establish a correspondence between the extracted explicit mesh representation and the implicit neural representation of the target scene. Users can first utilize well-developed mesh-based deformation methods to deform the mesh representation of the scene. Our method then utilizes user edits from the mesh representation to bend the camera rays by introducing a tetrahedra mesh as a proxy, obtaining the rendering results of the edited scene. Extensive experiments demonstrate that our framework can achieve ideal editing results not only on synthetic data, but also on real scenes captured by users.

• The paper presents a novel framework enabling geometry-based editing of NeRFs without retraining by aligning explicit triangular meshes with implicit neural representations.
• It leverages ARAP mesh deformation and a tetrahedral mesh proxy to transfer shape changes effectively to synthesized views, achieving high SSIM and PSNR scores.
• The approach outperforms previous methods by unlocking fine-grained, scalable 3D scene modifications with practical applications in VR, film, and gaming.

Analysis of "NeRF-Editing: Geometry Editing of Neural Radiance Fields"

The paper "NeRF-Editing: Geometry Editing of Neural Radiance Fields" introduces a method that empowers users to perform geometry-based edits on Neural Radiance Fields (NeRFs) without rebuilding the model, thus breathing new life into static 3D scenes through shape deformation. NeRFs have garnered considerable interest for their ability to generate novel view syntheses, yet user-controlled shape deformation remained largely unaddressed until this paper. Unlike previous methods which either focused on color modifications or simple object transformations like translation or rotation, this work fundamentally extends the editing capabilities to include geometric deformations within a scene.

The innovative aspect of this method is its establishment of a correspondence between explicit and implicit scene representations, specifically leveraging a triangular mesh extracted from the trained NeRF. Users can apply matured mesh deformation techniques, such as As-Rigid-As-Possible (ARAP) deformation, on this mesh. To transition these mesh transformations back to the neural representation, the approach introduces a tetrahedral mesh as a proxy, allowing for volumetric deformation transfer. This tetrahedral mesh effectively encapsulates the triangular mesh and enables the bending of camera rays to synthesize the altered views.

Key Numerical Results and Methodological Strengths

The authors conducted extensive experiments which affirm the robustness of the proposed framework across synthetic and real datasets. The method achieves favorable results in terms of Structural Similarity Index Measure (SSIM) and Peak Signal-to-Noise Ratio (PSNR), illustrating high fidelity and consistency even after significant scene modifications. Notably, the image synthesis post-edit does not require retraining, thereby enhancing the efficiency and practicality of such adaptations.

Comparative Analysis and Implications

Correspondence with existing approaches, such as EditingNeRF and ObjectNeRF, highlights the advancements made in the granular level and scope of possible modifications. Where earlier methods allowed for limited manipulations—primarily in static or segregated object spaces—this technique operates across general scenes, supporting detailed deformations articulated by user-defined constraints.

Theoretical and Practical Implications

Theoretically, this research expands the intersection of geometric modeling and neural network-based rendering, presenting a new paradigm for scene editing that is scalable and adaptable. On a practical level, it promises applications in virtual reality development, film production, and interactive gaming, where real-time and detailed model editing is crucial.

Future Directions in AI

The implications of this research extend beyond NeRFs. It offers a framework for integrating explicit geometric structures into neural implicit models, suggesting new opportunities in AI-driven 3D modeling and rendering. Future work may explore real-time rendering optimizations or the integration of relighting techniques to adjust appearances in relation to geometry modifications, thereby elevating the photorealism of rendered scenes.

In conclusion, "NeRF-Editing: Geometry Editing of Neural Radiance Fields" delivers a well-formulated advance in the field of 3D scene manipulation, merging intuitive user control with complex neural rendering capabilities, setting a promising course for future research and applications in computational graphics and AI-enhanced visualizations.