NeRF Scene Relighting for Day-to-Night Perception Adaptation
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Solution Overview
Problem
Machine learning models trained primarily on daytime scenes struggle to process image data showing nighttime scenes due to domain shift in lighting conditions, leading to poor performance in autonomous and semi-autonomous vehicles and other perception tasks.
Innovation Solution
A Neural Radiance Field (NeRF)-based machine learning model disentangles shape, reflectance, and illumination data from scenes, allowing images to be relit with new illumination parameters while preserving original geometry and textures, using a static head for static objects and a transient head for transient objects, and applying Spherical Harmonics coefficients to simulate different lighting conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a machine learning model is trained primarily on daytime scene image data, then the model achieves good performance for daytime perception tasks, but the model struggles to process nighttime scene image data due to domain shift in lighting conditions
Solution Approach 1:
The patent segments the scene representation into separate components: geometry (shape), reflectance (albedo), and illumination. By disentangling these factors, the model can process daytime training data and then adapt to nighttime conditions by modifying only the illumination component, without retraining on nighttime data. This segmentation allows the model to maintain high performance while gaining adaptability across different lighting conditions.
Solution Approach 2:
The patent changes the illumination parameters of the relit images to match nighttime conditions while preserving the underlying scene geometry and reflectance. By adjusting illumination parameters rather than retraining the entire model, the system achieves adaptability to nighttime scenes while maintaining the reliability established during daytime training.
2Adaptability or versatility
If existing techniques are used to change illumination conditions in images, then the illumination can be modified, but the relighting appears unrealistic and fails to preserve scene geometry and textures properly
Solution Approach 1:
The patent segments the image processing into distinct stages: geometry extraction, reflectance estimation, and illumination relighting. By separating these functions, the system can modify illumination parameters without distorting the underlying scene geometry or textures, achieving realistic relighting that preserves structural accuracy.
Solution Approach 2:
The patent introduces an intermediate scene representation that separates geometry, reflectance, and illumination components. This intermediary representation allows independent manipulation of illumination parameters while maintaining faithful reconstruction of scene geometry and textures, producing realistic relit images.
3Adaptability or versatility
If a model is trained on diverse illumination conditions, then the model achieves good performance across different lighting scenarios, but the training data becomes more complex and harder to obtain
Solution Approach 1:
The patent performs preliminary action by training the model once on daytime scenes to learn scene geometry and reflectance. After training, the system can generate relit images for any illumination condition by modifying parameters, eliminating the need to collect and train on diverse nighttime data. This preliminary training simplifies the data collection process while achieving broad adaptability.
Solution Approach 2:
The patent creates synthetic copies of the trained daytime scene model with modified illumination parameters. By copying the learned scene representation and adjusting illumination, the system generates realistic nighttime-like images without requiring actual nighttime training data, reducing training data complexity while maintaining adaptability.
Data Source
AI summary
A system comprises processors configured to generate rendered color data for a plurality of camera rays, and the processors are configured to: for each location in a 3-dimensional scene along the camera rays: apply static and transient heads of a trained ML model to location data to generate static and transient output data for a location; generate composite density data for the location based on static and transient density data for the location; and generate composite albedo data for the location; and generate the rendered color data for the camera ray based on shadow data, the composite density data, the composite albedo data, the static or transient normal vectors for the locations along the camera ray, and a set of spherical harmonics coefficients representing target illumination conditions; and generate a relit image based on the rendered color data for the plurality of camera rays.