Neural Basis Decomposition for Editable Neural Radiance Fields
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing neural rendering techniques, such as NeRF, entangle geometry and appearance in a black box volume encoding neural network, making it difficult to edit the appearance of synthesized scenes.
Innovation Solution
The introduction of neural basis decomposition allows for the separation of geometry and appearance, enabling the use of multiple neural networks to decompose the appearance of a scene into editable components, such as color and shading.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a black box volume encoding neural network is used to represent the scene, then the rendering quality is improved, but the ability to edit appearance is worsened
Solution Approach 1:
The patent segments the appearance representation into discrete basis functions (e.g., spherical harmonics, wavelets) that can be independently manipulated. Instead of using a monolithic black box neural network, the appearance is decomposed into separable components that can be edited individually while maintaining rendering quality.
Solution Approach 2:
The patent extracts the appearance information from the black box volume encoding and represents it separately using basis functions. This extraction allows the appearance to be isolated from the geometry representation, enabling independent editing of appearance properties without affecting the underlying 3D structure.
2Device complexity
If geometry and appearance are entangled in a single neural network, then the model complexity is reduced, but the flexibility for independent editing is worsened
Solution Approach 1:
The patent divides the scene representation into separate geometry and appearance components. The geometry is represented by one neural network while the appearance is represented by basis functions, allowing independent manipulation of each component without increasing overall system complexity.
Solution Approach 2:
The patent introduces basis functions as an intermediary representation between the neural network and the final rendering. This intermediary layer decouples the geometry and appearance representations, enabling flexible editing while maintaining a relatively simple overall model structure.
3Quantity of substance
If appearance is represented as a unified black box, then the storage requirements are reduced, but the ability to selectively edit appearance components is worsened
Solution Approach 1:
The patent segments the appearance representation into discrete basis function coefficients that can be stored separately. This segmentation allows selective editing of specific appearance components (e.g., lighting, material properties) while maintaining efficient storage through the compact basis function representation.
Solution Approach 2:
The patent represents appearance using parametric basis functions where individual parameters can be modified independently. This allows selective editing of appearance components by changing specific parameters without requiring storage of the entire appearance dataset, maintaining efficient storage while enabling targeted edits.
Data Source
AI summary
Embodiments of the present disclosure provide systems, methods, and computer storage media for generating editable synthesized views of scenes by inputting image rays into neural networks using neural basis decomposition. In embodiments, a set of input images of a scene depicting at least one object are collected and used to generate a plurality of rays of the scene. The rays each correspond to three-dimensional coordinates and viewing angles taken from the images. A volume density of the scene is determined by inputting the three-dimensional coordinates from the neural radiance fields into a first neural network to generate a 3D geometric representation of the object. An appearance decomposition is produced by inputting the three-dimensional coordinates and the viewing angles of the rays into a second neural network.