Object-Wise NeRF Scene Decomposition for Editable 3D Rendering
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Solution Overview
Problem
Existing techniques for generating 3D representations of scenes, such as photogrammetry and Neural Radiance Fields (NeRFs), fail to effectively handle complex lighting elements and do not allow for individual manipulation or editing of objects within a scene, treating the scene as a whole rather than discrete entities.
Innovation Solution
The technique involves determining a different radiance field function for each neural radiance field (NeRF) associated with individual objects in a scene, generating a combined radiance field function, and computing a decomposition loss to modify the NeRFs, allowing for individual editing and manipulation of objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a single NeRF represents an entire scene as a whole, then the scene can be rendered from different viewpoints, but all objects in the scene cannot be individually manipulated or edited
Solution Approach 1:
The patent divides the scene representation into multiple separate NeRFs, each corresponding to a specific object or region in the scene. This segmentation allows individual objects to be independently manipulated, edited, and rendered while maintaining the overall scene coherence through compositional rendering of the separated NeRFs.
2Reliability
If photogrammetry is used to create a 3D model from multiple 2D representations, then the scene geometry can be reconstructed, but complex lighting elements such as reflections and reflective surfaces are not handled properly
Solution Approach 1:
The patent transitions from geometric parameter-based modeling (photogrammetry) to radiance field-based modeling (NeRF), fundamentally changing the representation parameters from simple 3D geometry to continuous 5D radiance fields that capture appearance, lighting, and material properties. This enables proper handling of complex lighting elements and reflective surfaces.
3Ease of operation
If photogrammetry models the entire scene as an indivisible whole, then the 3D model can be generated from triangulation, but subsequent editing of the scene is complicated or wholly prevented
Solution Approach 1:
The patent segments the scene into multiple independent NeRF representations corresponding to different objects. This segmentation enables individual objects to be easily edited, added, or removed while maintaining the overall scene structure through compositional rendering, thus improving ease of operation without compromising structural integrity.
Solution Approach 2:
The patent creates a dynamic scene representation where individual NeRF components can be independently modified, added, or removed. This dynamic structure allows flexible scene editing while maintaining coherence through the compositional rendering framework that combines the individual object representations.
Data Source
AI summary
The present invention sets forth a technique for performing scene decomposition. This technique includes determining, based on a plurality of two-dimensional (2D) representations of a three-dimensional (3D) scene, a different radiance field function for each of a plurality of neural radiance fields (NeRFs). The technique also includes generating a combined radiance field function based on the radiance field functions associated with the plurality of NeRFs. The technique further includes generating a color value for a given 3D location and viewing angle in the 3D scene. The technique further includes computing a decomposition loss based on the difference between the color value and a ground truth color value associated with the 3D location and viewing angle. The technique further includes modifying at least one of the plurality of NeRFs based on the decomposition loss such that each NeRF is associated with a different object in the 3D scene.