Keyframe Selection for Robust Absolute Pose Regression in Markerless Mobile Augmented Reality
แนวคิดหลัก
Identifying reliable pose estimations from Absolute Pose Regression (APR) methods to improve accuracy and robustness, while minimizing computational overhead.
บทคัดย่อ
The paper introduces KS-APR, a pipeline that assesses the reliability of an estimated pose from APR methods with minimal overhead. The key insights are:
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KS-APR uses the 6DoF pose estimated by the APR to identify the closest images in the training set and evaluates their similarity through feature-based methods. This allows identifying images close to the training set to trigger absolute pose estimation, while relying on local visual-inertial odometry tracking between these images.
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KS-APR is APR-agnostic, allowing it to reinforce most existing APR algorithms with minimal computation and storage overhead. It discards input frames that would lead to inaccurate results, improving the accuracy and robustness of the underlying APR.
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Experiments on indoor and outdoor datasets show that KS-APR can reduce the median position error by up to 28.6% and the median orientation error by up to 22% for state-of-the-art APR methods, while eliminating large errors on some scenes.
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KS-APR: Keyframe Selection for Robust Absolute Pose Regression
สถิติ
The median position error can be reduced by up to 28.6% and the median orientation error by up to 22% for state-of-the-art APR methods.
KS-APR can eliminate large errors (>5m, >10°) on some scenes.
คำพูด
"KS-APR favours reliability over frequency, discarding the unreliable poses."
"KS-APR is APR-agnostic, which allows reinforcing most existing algorithms with minimal computation and storage overhead."
สอบถามเพิ่มเติม
How can KS-APR be extended to handle dynamic environments where the scene changes over time
KS-APR can be extended to handle dynamic environments by incorporating a mechanism to adapt to changes in the scene over time. One approach could be to implement a dynamic updating system that periodically reevaluates the keyframes based on the evolving environment. This system could continuously monitor the reliability of the keyframes and update them as needed to account for changes in the scene. Additionally, integrating a mechanism for outlier detection could help identify and filter out keyframes that no longer accurately represent the scene due to dynamic changes. By incorporating real-time feedback mechanisms and adaptive algorithms, KS-APR can effectively handle dynamic environments in mobile AR applications.
What are the limitations of the feature-based similarity metric used in KS-APR, and how could it be improved
The feature-based similarity metric used in KS-APR has certain limitations that could be addressed for further improvement. One limitation is the reliance on traditional feature extraction and matching techniques, which may not capture complex spatial relationships accurately in all scenarios. To enhance the metric, advanced feature extraction methods such as deep learning-based feature descriptors could be implemented to improve the accuracy and robustness of the similarity assessment. Additionally, incorporating semantic information or contextual cues into the similarity metric could provide a more comprehensive understanding of scene similarity beyond just visual features. By enhancing the feature-based similarity metric with advanced techniques and additional contextual information, the overall performance of KS-APR could be further optimized.
How could KS-APR be integrated with other visual localization techniques, such as structure-based methods, to further enhance the accuracy and robustness of mobile AR applications
Integrating KS-APR with other visual localization techniques, such as structure-based methods, can enhance the accuracy and robustness of mobile AR applications. By combining KS-APR with structure-based localization approaches, the system can leverage the strengths of both methods to improve overall performance. One way to integrate these techniques is to use the structure-based method for initial localization and then apply KS-APR for continuous refinement and reliability assessment of the camera pose. This hybrid approach can provide a more comprehensive and accurate localization solution by combining the strengths of both methods. Additionally, incorporating sensor fusion techniques to merge data from visual and inertial sensors with KS-APR outputs can further enhance the system's accuracy and robustness in dynamic environments. By integrating KS-APR with structure-based methods and sensor fusion techniques, mobile AR applications can achieve higher localization accuracy and reliability.
