Deferred Neural Lighting for Augmented Image Generation
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Solution Overview
Problem
Current virtual world simulation technologies face challenges in generating realistic augmented images, particularly in replicating real-world lighting conditions, which affects the accuracy of shadows and overall realism in virtual environments.
Innovation Solution
A computer-implemented method using a neural deferred rendering model processes panoramic images to generate source and target lighting representations, allowing for the creation of augmented images with accurate lighting appearances by relighting scenes, enabling modifications such as changing sun positions and intensity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional rendering methods are used to generate augmented images, then the processing speed is fast, but the lighting appearance does not match the augmentation and shadows are unrealistic
Solution Approach 1:
The rendering process is segmented into two distinct phases: (1) a fast traditional rendering pass that generates base image buffers without complex lighting calculations, and (2) a neural network-based relighting pass that adds realistic lighting and shadows. This segmentation allows the system to achieve photorealistic lighting accuracy while maintaining processing efficiency by separating the speed-critical path from the quality-critical path.
Solution Approach 2:
A neural deferred rendering model acts as an intermediary between the traditional rendering output and the final augmented image. This intermediary component takes the base image buffers and applies learned lighting transformations to produce realistic shadows and lighting effects that match the augmentation, bridging the gap between fast rendering and photorealistic quality.
2Manufacturing precision
If realistic lighting is implemented in augmented images, then the shadows and realism improve, but the computational processing time increases
Solution Approach 1:
The system performs preliminary rendering of image buffers using traditional methods before applying neural relighting. By pre-computing the base scene geometry and lighting-independent properties, the system avoids redundant calculations during the final relighting stage, reducing overall processing time while maintaining shadow accuracy.
Solution Approach 2:
The neural deferred rendering model applies lighting corrections selectively to enhance only the critical shadow and lighting regions rather than re-rendering the entire image at high fidelity. This partial action approach focuses computational resources on the most visually important aspects of lighting realism.
3Manufacturing precision
If the real world is modified with augmented objects, then the virtual world realism improves, but the lighting consistency between real and augmented elements deteriorates
Solution Approach 1:
The neural deferred rendering model uses the source lighting representation derived from the real-world panoramic image as feedback to guide the relighting of augmented elements. The model learns to match the lighting characteristics of the real scene and applies these learned patterns to augmented objects, ensuring that shadows and illumination intensity are consistent between real and synthetic elements.
Solution Approach 2:
The system dynamically adjusts lighting parameters such as illumination intensity, shadow direction, and color temperature based on the target lighting representation. By changing these parameters to match the real-world scene conditions, the system achieves uniform lighting appearance across both real and augmented elements in the composite image.
Data Source
AI summary
Deferred neural lighting in augmented image generation includes performing operations. The operations include generating a source light representation of a real-world scene from a panoramic image of the real-world scene, augmenting the real-world scene in an object representation of the real-world scene to generate an augmented scene, and processing the augmented scene to generate augmented image buffers. The operations further include selecting a target lighting representation identifying a target light source, processing, by a neural deferred rendering model, the augmented image buffers, the source lighting representation, and a target lighting representation to generate an augmented image having a lighting appearance according to the target light source and outputting the augmented image.