Staggered Feature Extraction for Low-Latency Multi-Camera Alignment
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Solution Overview
Problem
Challenges arise in aligning image content from multiple cameras in mixed-reality systems, particularly when integrating system and external cameras, due to insufficient detectable features and latency issues, which affect hologram placement and generation.
Innovation Solution
The image alignment is performed using staggered feature extraction, where a set of features from a first image are reused to align with a second image, and if the correspondence threshold is not met, a different permutation of features is used to perform a new alignment operation, avoiding latency by accessing pre-generated features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If feature extraction is performed on every image to ensure alignment accuracy, then manufacturing precision of image alignment is improved, but loss of time increases due to processing latency
Solution Approach 1:
The patent performs feature extraction on the second image in advance, before the first image is fully processed. The extracted features are stored and reused when the first image becomes available for alignment, eliminating the need to wait for complete processing of both images before performing feature extraction and alignment operations.
Solution Approach 2:
The patent implements a dynamic feature extraction strategy where the system adapts its processing based on image quality and feature detectability. When sufficient features are detected in the second image, extraction is performed early; when features are insufficient, the system waits for better images or uses alternative approaches, making the processing timeline flexible rather than rigid.
2Productivity
If feature extraction is skipped to reduce processing time, then productivity is improved, but manufacturing precision of image alignment deteriorates due to insufficient features
Solution Approach 1:
The system performs feature extraction on the second image in advance and stores the results. When the first image is ready for alignment, the pre-extracted features from the second image are immediately available for use, eliminating the need to perform feature extraction at the moment of alignment and thus maintaining high frame rates.
Solution Approach 2:
The patent maintains continuous feature extraction operations on the second image stream, ensuring that features are always available when needed for alignment with the first image. This continuous preparation of features ensures that alignment operations can proceed without interruption, maintaining high productivity while ensuring precision is never compromised due to feature unavailability.
3Reliability
If multiple permutations of features are tried to ensure alignment success, then reliability is improved, but use of energy increases due to additional processing
Solution Approach 1:
The system performs feature extraction on the second image in advance and evaluates the quality and quantity of detected features. Based on this preliminary assessment, the system can determine whether the extracted features are sufficient for reliable alignment, avoiding the need to try multiple permutations when features are already adequate.
Solution Approach 2:
The patent implements a feedback mechanism where the system evaluates the quality of extracted features and uses this information to decide whether to proceed with alignment using those features or to try alternative approaches. This feedback loop ensures that multiple permutations are only attempted when necessary, optimizing energy consumption while maintaining high alignment success rates.
Data Source
AI summary
Techniques for performing image alignment between a first image generated by a first camera and a second image generated by a second camera are disclosed. The image alignment is performed using staggered feature extraction in which a set of features are reused to align the second image with the first image. A first set of features are identified from within the first image, and a second set of features, which were previously detected within the second image, are accessed. The second set of features were previously used at least once to perform a previous image alignment operation. A current image alignment operation is performed by using the first set of features and by reusing the second set of features to align the first image with the second image.