Automatic Key-Image Database Management for Camera Pose Tracking
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Solution Overview
Problem
Existing methods for managing keyframe databases in augmented reality laparoscopic procedures are cumbersome, requiring frequent user intervention and consuming significant hardware resources, leading to suboptimal performance and user experience.
Innovation Solution
A method and device for automated management of keyframes using multi-criteria selection based on reference point matching, pose estimation quality, and image sharpness, allowing new keyframes to be added to the database without impacting real-time video processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual addition of key images is used to complete the 3D model, then tracking coverage is improved, but user independence deteriorates and operation complexity increases
Solution Approach 1:
The system automatically manages the keyframe database by detecting when current images should be added as new keyframes based on similarity thresholds and tracking performance metrics, eliminating the need for manual user intervention while maintaining comprehensive tracking coverage
Solution Approach 2:
The system continuously monitors tracking performance and image similarity metrics, using this feedback to automatically determine when to add new keyframes to the database, creating a closed-loop system that adapts to tracking needs without user input
2Ease of operation
If automatic selection of new key images is implemented, then user independence is improved, but hardware resource consumption increases
Solution Approach 1:
The system uses adjustable similarity thresholds and selection criteria parameters to control the frequency and conditions under which new keyframes are added, allowing optimization of the balance between automation level and computational resource consumption
Solution Approach 2:
The system implements selective automatic keyframe addition only when specific conditions are met (tracking loss detected, similarity threshold exceeded), rather than continuously processing all images, reducing overall hardware resource consumption while maintaining effectiveness
3Measurement precision
If frequent keyframe database updates are performed, then tracking accuracy in changing areas is improved, but processing time increases
Solution Approach 1:
The system performs keyframe database updates periodically based on detected tracking performance degradation or image similarity thresholds rather than continuously, reducing processing overhead while maintaining accuracy in dynamically changing surgical fields
Solution Approach 2:
The system proactively adds keyframes before complete tracking loss occurs by monitoring similarity metrics and adding keyframes when thresholds are approached, preventing tracking failures and reducing the need for corrective processing
Data Source
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AI summary
The invention relates to a method for real-time tracking of the pose of a camera with respect to a 3D model comprising a key-image database that comprises a first execution thread for estimating the current pose of the camera from a selected key image, characterised in that the method further comprises a step of analysing the quality of the estimation of the pose of the camera from the image stored in a buffer memory, and a step of analysing the sharpness of the image stored in the buffer memory from data representative of the movement of the camera, and if the quality of the estimation of the pose of the camera from the stored image and the sharpness of the stored image comply with predetermined criteria, a step of adding the image stored in the buffer memory to the key-image database, as a new key image.