Gaze-Based Depth Image Super-Resolution for Real-Time AR
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Solution Overview
Problem
Augmented reality systems face high computational complexity and processing delays due to the need for accurate real-time processing of three-dimensional images, particularly in predicting depth images, which is challenging for real-time applications.
Innovation Solution
A method and electronic device that utilize gaze-based foveated depth image super resolution, segmenting images into regions with varying levels of quality based on gaze point information, using AI networks to process these regions differently, reducing computational complexity and processing delay while maintaining accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the entire image is processed to obtain a depth image with high accuracy, then the depth image accuracy is improved, but the computational complexity and processing time increase significantly
Solution Approach 1:
The image is divided into multiple regions based on gaze point information, with different processing strategies applied to each region. The first image region (foveal region) centered on the gaze point undergoes full processing for high accuracy, while the second image region (peripheral region) uses simplified processing, thereby reducing overall computational complexity while maintaining depth image accuracy in the critical foveal region.
Solution Approach 2:
Different processing qualities are applied to different regions of the image based on their importance. The foveal region (first image region) receives high-quality processing with full computational resources to ensure high depth accuracy, while the peripheral region (second image region) receives lower-quality processing with reduced computational resources, optimizing the trade-off between accuracy and complexity.
2Measurement precision
If the entire image is processed to obtain a depth image with high accuracy, then the depth image accuracy is improved, but the processing time increases
Solution Approach 1:
The image processing is segmented into two parallel paths: one for the foveal region (first image region) and another for the peripheral region (second image region). This segmentation allows the system to process only the critical foveal region with high computational intensity to maintain depth accuracy, while using simplified processing for the peripheral region, thereby significantly reducing overall processing time.
Solution Approach 2:
Instead of processing the entire image with full computational resources, the system applies partial processing - full processing is performed only on the foveal region (first image region) where high depth accuracy is critical, while simplified processing is applied to the peripheral region (second image region), reducing the total processing time while maintaining necessary accuracy in the important region.
3Device complexity
If uniform processing is applied to all image regions, then the processing simplicity is maintained, but the processing efficiency decreases
Solution Approach 1:
The system applies different processing strategies to different regions: the foveal region (first image region) uses a first neural network with full processing for high accuracy, while the peripheral region (second image region) uses a second neural network with simplified processing. This local differentiation improves processing efficiency by allocating computational resources based on regional importance, rather than applying uniform processing to the entire image.
Solution Approach 2:
The image is segmented into foveal and peripheral regions, each processed by dedicated neural networks optimized for their specific requirements. This segmentation enables the system to process the critical foveal region with high fidelity while using more efficient simplified processing for the peripheral region, thereby improving overall processing efficiency without significantly increasing system complexity.
Data Source
AI summary
A method includes obtaining a first image, where the first image includes an RGB image and a first depth image as components of the first image, obtaining at least two image regions by processing the first image through an artificial intelligence (AI) network based on gaze point information, and obtaining a second depth image based on the at least two image regions, where, in the at least two image regions, image qualities of respective image regions are different, and a resolution of the first depth image is lower than a resolution of the second depth image.