Wearable Display Depth Reprojection With Region-Based Pixel Setting
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing wearable devices struggle with efficiently and quickly setting depth values for pixels, leading to user fatigue and reduced experience due to resource-intensive full-depth reprojection processes.
Innovation Solution
The wearable device employs adaptive multi-planar reprojection, where depth values are set differently for important and less important areas, using cameras and sensors to identify reference and predetermined depth values, optimizing resource allocation and enhancing user experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If full-depth reprojection process is used to set depth values for all pixels, then depth accuracy is improved, but processing time and resource consumption increase
Solution Approach 1:
The image is divided into multiple regions (first region, second region, third region) based on depth characteristics. Different depth setting strategies are applied to each region: the first region uses reference depth values from peak depth value identification, the second region uses interpolated depth values, and the third region uses default depth values. This segmentation allows the system to achieve adequate depth accuracy for important regions while significantly reducing processing time by applying simplified methods to less critical regions.
Solution Approach 2:
Different quality levels of depth processing are applied to different regions of the image. The first region (containing important objects) receives high-quality depth processing using reference depth values identified from peak values, while the second and third regions receive lower-quality processing using interpolated or default depth values. This local quality approach ensures that depth accuracy is maintained where it matters most while reducing overall computational burden.
2Measurement precision
If full-depth reprojection process is used to set depth values for all pixels, then depth accuracy is improved, but resource consumption increases
Solution Approach 1:
The image is divided into multiple regions (first region, second region, third region) based on depth characteristics. Different depth setting strategies are applied to each region: the first region uses reference depth values from peak depth value identification, the second region uses interpolated depth values, and the third region uses default depth values. This segmentation allows the system to achieve adequate depth accuracy for important regions while significantly reducing processing time by applying simplified methods to less critical regions.
Solution Approach 2:
Different quality levels of depth processing are applied to different regions of the image. The first region (containing important objects) receives high-quality depth processing using reference depth values identified from peak values, while the second and third regions receive lower-quality processing using interpolated or default depth values. This local quality approach ensures that depth accuracy is maintained where it matters most while reducing overall computational burden.
3Ease of operation
If adaptive multi-planar reprojection is used to differentiate depth values for different areas, then user experience is improved, but processing complexity increases
Solution Approach 1:
The image is divided into multiple regions (first region, second region, third region) based on depth characteristics. Different depth setting strategies are applied to each region: the first region uses reference depth values from peak depth value identification, the second region uses interpolated depth values, and the third region uses default depth values. This segmentation allows the system to achieve adequate depth accuracy for important regions while significantly reducing processing time by applying simplified methods to less critical regions.
Solution Approach 2:
The system dynamically determines which pixels belong to which region based on depth value analysis and peak value identification. The processing approach adapts to the content of each image, automatically adjusting the complexity of depth processing applied to different regions. This dynamic adaptation improves user experience by providing appropriate depth accuracy where needed while managing processing complexity through automated region classification.
Data Source
AI summary
A wearable device includes a display assembly comprising one or more displays, cameras, sensors, memory comprising one or more storage media and storing instructions, and at least one processor comprising processing circuitry. The instructions, when executed by the at least one processor individually or collectively, cause the wearable device to obtain an image, identify depth values of pixels using obtained depth information, identify the number of pixels, identify a reference depth value among the depth values of the pixels, identify a first portion of the pixels, identify a second portion of the pixels, and based on setting the depth values of the first portion of the pixels to the reference depth value and based on setting depth values of the second portion of the pixels to a predetermined depth value, display, via the display assembly, a screen corresponding to the image.