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Bringing the Clinic Home: An At-Home Multi-Modal Data Collection Ecosystem to Support Adaptive Deep Brain Stimulation

Bringing the Clinic Home: An At-Home Multi-Modal Data Collection Ecosystem to Support Adaptive Deep Brain Stimulation

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Camera-pose estimation via projective Newton optimization on the manifold.

Michel Sarkis¹, Klaus Diepold

¹Institute for Data Processing, Technische Universität München, Munich, Germany. michel.sarkis@sony.de

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|December 14, 2011

Summary

This summary is machine-generated.

This study introduces a new projective Newton algorithm for accurate camera pose estimation in computer vision. The method refines camera pose by leveraging the special Euclidean group, reducing computational complexity by 60% and improving accuracy.

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Bringing the Clinic Home: An At-Home Multi-Modal Data Collection Ecosystem to Support Adaptive Deep Brain Stimulation

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Published on: July 14, 2023

Area of Science:

Computer Vision
Robotics
Geometric Deep Learning

Background:

Accurate camera pose estimation is crucial for applications like autonomous navigation and augmented reality.
Existing methods often face challenges with computational efficiency and accuracy in dynamic environments.

Purpose of the Study:

To develop a novel projective Newton algorithm for refining camera pose estimates.
To leverage the properties of the special Euclidean group as a Riemannian manifold for pose optimization.

Main Methods:

Derivation of a projective Newton algorithm operating on the manifold of the special Euclidean group.
Utilizing the Lie algebra to compute gradients and Hessians within the tangent space.
Implementing a version with homeomorphic parameterization for enhanced stability.
Testing on simulated and real-world image datasets.

Main Results:

Achieved a 60% reduction in computational complexity compared to standard Newton minimization.
Obtained more accurate results than the Levenberg-Marquardt algorithm with similar computational complexity.
Demonstrated the algorithm's effectiveness on diverse datasets.

Conclusions:

The proposed projective Newton algorithm offers a significant improvement in both accuracy and computational efficiency for camera pose estimation.
The manifold-based approach provides a robust framework for handling 3-D rigid motion in computer vision.
This method advances the state-of-the-art in real-time camera tracking and scene reconstruction.