HARD-PnP: PnP Optimization Using a Hybrid Approximate Representation.

Area of Science:

• Computer Vision
• Computational Geometry
• Robotics

Background:

• The generalized reprojection error is the standard for multiview geometry.
• Existing methods for solving the Perspective-n-Point (PnP) problem can be computationally intensive and susceptible to local minima.

Purpose of the Study:

• To propose a novel Hybrid Approximate Representation (HAR) for efficient and robust multiview geometry computations.
• To introduce a joint minimization scheme, HAR-Descent, for solving the PnP problem.

Main Methods:

• Developed a Hybrid Approximate Representation (HAR) by unifying efficient approximations of the generalized reprojection error.
• Implemented a joint minimization scheme, HAR-Descent, applying approximations simultaneously across multiple parameter spaces.
• Conducted extensive evaluations on synthetic benchmarks and real-world datasets.

Main Results:

• HAR-Descent achieves runtime complexity comparable to the fastest O(n) techniques.
• The proposed method is up to 10 times faster than current state-of-the-art minimization approaches.
• Achieved superior accuracy, outperforming all 9 previously tested techniques across 90 experiments.

Conclusions:

• The Hybrid Approximate Representation (HAR) and HAR-Descent offer a state-of-the-art solution for the PnP problem.
• The technique provides significant speedups and accuracy improvements in multiview geometry.
• HAR-Descent demonstrates robustness to approximation errors and local minima.