Relightable NeRF Rendering Under Arbitrary Lighting
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing neural radiance fields (NeRF) models are limited in their ability to generate realistic 3D representations of scenes under varying illumination conditions, as they are trained on specific lighting conditions and fail to scale to environments with different illumination.
Innovation Solution
A technique involving a relightable NeRF model that includes a neural radiance field (NeRF) and a 'one light at a time' (OLAT) model, trained on 2D representations of a scene under varying lighting conditions, to generate a combined radiance field function that produces color and density values for any given 3D location and viewing angle, allowing rendering under arbitrary lighting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a NeRF model is trained on 2D representations captured under specific illumination conditions, then the model can generate realistic 2D representations from arbitrary viewpoints under those specific lighting conditions, but the model cannot be rendered realistically in environments with different illumination conditions
Solution Approach 1:
The patent segments the rendering process into two distinct models: a NeRF model that captures scene geometry and appearance under specific lighting, and a relighting model that separately handles illumination transformation. This segmentation allows each model to specialize - the NeRF model achieves high accuracy for the training lighting conditions while the relighting model provides adaptability to new lighting environments, resolving the contradiction between rendering accuracy and adaptability.
Solution Approach 2:
The patent introduces an intermediary relighting model that acts as a mediator between the original NeRF rendering and the target illumination conditions. This intermediary model receives the NeRF output and transforms it to match desired lighting conditions, allowing the system to maintain the high accuracy of the original NeRF while adapting to various illumination environments, thus resolving the contradiction.
2Reliability
If a NeRF model is trained to capture scene characteristics including illumination, then the model learns both object characteristics and lighting characteristics, but the output becomes dependent on the training illumination conditions
Solution Approach 1:
The patent segments the learning process into two separate models: one that learns scene geometry and appearance (NeRF) and another that learns illumination characteristics (relighting model). This segmentation allows the first model to maintain reliability and consistency with training data while the second model provides the versatility to adapt to different environmental lighting conditions, resolving the contradiction between reliability and adaptability.
Solution Approach 2:
The patent extracts the illumination characteristics from the original NeRF training process and places them into a separate relighting model. This extraction allows the main NeRF model to focus on learning scene geometry and appearance consistently, while the extracted lighting information is handled separately by the relighting model, enabling scalability to different environments without compromising the reliability of the core scene representation.
Data Source
AI summary
The present invention sets forth a technique for generating two-dimensional (2D) renderings of a three-dimensional (3D) scene from an arbitrary camera position under arbitrary lighting conditions. This technique includes determining, based on a plurality of 2D representations of a 3D scene, a radiance field function for a neural radiance field (NeRF) model. This technique further includes determining, based on a plurality of 2D representations of a 3D scene, a radiance field function for a “one light at a time” (OLAT) model. The technique further includes rendering a 2D representation of the scene based on a given camera position and illumination data. The technique further includes computing a rendering loss based on the difference between the rendered 2D representation and an associated one of the plurality of 2D representations of the scene. The technique further includes modifying at least one of the NeRF and OLAT models based on the rendering loss.