HMD Passthrough Colorization Using Affinity Matrices
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Artificial reality systems, such as HMDs, obstruct users' views of their physical surroundings, and existing passthrough features face challenges in accurately rendering color-consistent images due to resource limitations and misalignment of cameras with the user's eyes, leading to noise and inconsistencies in color generation.
Innovation Solution
A machine-learning model processes grayscale images to generate colored versions, with post-processing techniques using affinity matrices to improve color consistency by adjusting pixel colors based on spatial and temporal relationships between pixels, and eigenvectors are used to optimize the process for efficient computation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If a machine-learning model is used to colorize grayscale images, then color information is added to passthrough images, but noise and color inconsistencies are introduced across objects, time, and stereo images
Solution Approach 1:
The system uses affinity matrices to compute relationships between pixels and applies feedback loops that iteratively adjust color values. The affinity-determination module continuously refines color assignments by comparing pixel affinities across spatial, temporal, and stereo dimensions, ensuring color consistency while preserving the colorization capability.
Solution Approach 2:
The system changes the parameter representation from raw pixel colors to affinity-based weighted averages. By transforming the color determination process to use affinity matrices that encode spatial, temporal, and stereo relationships, the system maintains color consistency across different dimensions while still introducing meaningful color information.
2Reliability
If affinity matrices are used to enforce color consistency across all pixels, then color consistency is improved, but computational complexity and memory requirements increase prohibitively
Solution Approach 1:
The system segments the affinity matrix computation by focusing only on relevant pixel relationships. Instead of computing full N×N affinity matrices for all pixels, the system identifies and processes only the necessary affinity relationships based on spatial proximity, temporal continuity, and stereo correspondence, significantly reducing computational complexity.
Solution Approach 2:
The system applies partial affinity enforcement by focusing computational resources on critical pixel relationships that most impact color consistency. Rather than uniformly processing all possible pixel pairs, the system selectively applies affinity constraints to pixels that are most likely to exhibit color inconsistencies, achieving good results with reduced computation.
3Ease of operation
If real-time colorization is implemented for passthrough images, then user experience is enhanced, but resource consumption on the HMD increases
Solution Approach 1:
The system performs preliminary computations during idle periods or when resources are available, pre-computing affinity matrices and color adjustments that can be applied later. This allows the system to prepare colorization data in advance, reducing the computational burden during real-time passthrough rendering and lowering instantaneous energy consumption.
Solution Approach 2:
The system applies colorization selectively to regions of the image that benefit most from color information while skipping or simplifying processing in regions where color is less critical. By applying different processing quality to different image regions, the system enhances user experience in important areas while conserving energy in less critical areas.
Data Source
AI summary
In one embodiment, a computing system may access a first grayscale image and a second grayscale image. The system may generate a first color image and a second color image based on the first grayscale image and the second grayscale image, respectively. The system may generate affinity information based on the first grayscale image and the second grayscale image, the affinity information identifying relationships between pixels of the first grayscale image and pixels of the second grayscale image. The system may modify the color of the first color image and the second color image based on the affinity information. The system may generate a first visual output based on the modified first color image and a second visual output based on the modified second color image.