Learning Object-Compositional Neural Radiance Field for Editable Scene Rendering (2109.01847v1)

Abstract: Implicit neural rendering techniques have shown promising results for novel view synthesis. However, existing methods usually encode the entire scene as a whole, which is generally not aware of the object identity and limits the ability to the high-level editing tasks such as moving or adding furniture. In this paper, we present a novel neural scene rendering system, which learns an object-compositional neural radiance field and produces realistic rendering with editing capability for a clustered and real-world scene. Specifically, we design a novel two-pathway architecture, in which the scene branch encodes the scene geometry and appearance, and the object branch encodes each standalone object conditioned on learnable object activation codes. To survive the training in heavily cluttered scenes, we propose a scene-guided training strategy to solve the 3D space ambiguity in the occluded regions and learn sharp boundaries for each object. Extensive experiments demonstrate that our system not only achieves competitive performance for static scene novel-view synthesis, but also produces realistic rendering for object-level editing.

• The paper introduces a dual-branch model that separates scene and object representations to enable flexible scene editing.
• It employs a scene-guided training method using 2D instance masks to manage occlusions and ensure clear object boundaries.
• Experimental results on ScanNet and custom datasets demonstrate realistic, high-fidelity renderings competitive with state-of-the-art methods.

Object-Compositional Neural Radiance Field for Editable Scene Rendering

The paper "Learning Object-Compositional Neural Radiance Field for Editable Scene Rendering" introduces an advanced neural rendering architecture that enables the editing of real-world scenes. This novel system addresses the limitations of existing rendering techniques, which typically fail to support object manipulation tasks such as rotating, moving, or duplicating objects within a scene. The centerpiece of this research is an innovative two-pathway model leveraging a scene branch and an object branch that aim to encode entire scene geometry and individual objects, respectively.

Key Contributions and Methodology

The authors present a comprehensive framework based on NeRF (Neural Radiance Fields), cleverly partitioned into two distinct branches:

1. Scene Branch: Encodes the geometry and appearance of the entire scene, supporting the rendering of the uneditable components and assisting in identifying occluded regions in conjunction with the object branch.
2. Object Branch: Focused on encoding individual objects using learnable object activation codes. This strategy allows for rendering each object in isolation from the rest of the scene, thereby facilitating various object-level operations.

To train this dual-branch architecture in cluttered environments, the authors devised a scene-guided training methodology to tackle 3D space ambiguities in occluded areas. This approach prevents unwanted artifacts and hallucinations in the object branch by leveraging scene branch outputs to identify occluded regions and ensure distinct object boundaries. The method requires only a collection of posed images and rough 2D instance masks as input, streamlining the data requirements traditionally associated with object rendering tasks.

Experimental Evaluation

The proposed system was evaluated on both synthetic and real-world datasets, prominently featuring the ScanNet and a custom ToyDesk dataset. The experiments demonstrated that the system could perform high-fidelity novel-view synthesis on par with the current state of the art, such as SRN and NeRF. Moreover, the object's part of the neural radiance field effectively generated highly realistic renderings for individual objects, even beneath conditions of partial occlusion.

Results and Implications

1. Rendering Quality: The paper asserts that the proposed method achieves competitive performance in static scene rendering and excels in producing realistic renderings for object-level manipulations, thanks in part to the hybrid space embedding and the novel scene-guided learning strategy.
2. Editability: The object-compositional feature significantly enhances the system's capacity for scene editing applications, such as augmented reality and virtual reality experiences, where dynamic interaction with 3D environments is requisite.
3. Implications for Future Research: The architecture provides new opportunities in AI-powered scene editing by leveraging object compositionality in neural radiance fields, which can be further extended to address more complex lighting models, integrate physical interactions, or enhance real-time performance.

Conclusion

This paper successfully bridges the gap between high-quality scene rendering and object-level scene editability, demonstrating that neural rendering systems can be designed to accommodate complex editing tasks. By disentangling scene representations into object-specific neural codes, the system enables a level of detail and flexibility previously unattainable in the domain of neural scene rendering. Reflecting on the accomplishments of this work, future research could focus on scaling such systems to larger environments or enhancing computational efficiency to further broaden their practical applicability.